{"as_of":"2026-08-07T22:38:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0d0ebab048f7330115d104e4f8e2183ec22c042845733da28e8f286a04905ec4","coverage":[{"denominator":107,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T11:30:59.978628Z","state":"measured"},{"denominator":100,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":100,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2506.06353/citation-record","integrity":"/paper/2506.06353/integrity","json":"/paper/2506.06353/citation-record.json","paper":"/paper/2506.06353"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:50.783079Z","title":"Attention Mechanism, Transformers, BERT, and GPT: Tutorial and Survey,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:50.783079Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:8eae6c9c331fcf53d7424243684b6fccd2795ec242f36813bac198a81d287ebe","observation_id":"591cb8f2-3c93-4250-ba71-bcf3ecd6bda4","resolution":{"observed_at":"2026-08-07T11:30:50.783079Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.11943","last_updated":"2021-03-22T15:34:39Z","snapshot_observed_at":"2026-08-04T14:58:50.151028Z","submitted_at":"2021-03-22T15:34:39Z","title":"BERT: A Review of Applications in Natural Language Processing and Understanding","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.11943","snapshot_observed_at":"2026-08-07T11:30:50.862480Z","title":"Bert: a review of applications in natural language processing and understanding,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:50.862480Z"},"links":{"cited_paper":"/paper/2103.11943","citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:7542622032c6ad488ade395222da37910522223054f00e4e377514ffbc18778d","observation_id":"647798ed-14d1-425d-be7b-3c2a0a3c4395","resolution":{"observed_at":"2026-08-07T11:30:50.862480Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:50.937935Z","title":"What does BERT learn about the structure of language?","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:50.937935Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:7b6eeebcb3162c97c304478bff0b54165ab2eac13788a2412b08d0f0b3fae842","observation_id":"fd825d56-5288-4744-8656-1a2ddf2a9de9","resolution":{"observed_at":"2026-08-07T11:30:50.937935Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:51.016109Z","title":"Brain com- puter interfaces, a review,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:51.016109Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:4ad8dc018f2fda789988017c34f9c57ad9af91947ddbb5805612beb0e514d715","observation_id":"b7c9b873-46cd-4756-8977-fc5db1c3633a","resolution":{"observed_at":"2026-08-07T11:30:51.016109Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:51.106031Z","title":"Brain- computer interface technology: a review of the first international meeting,","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:51.106031Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:3f67eba539c66ed069e244b5c509d619a465ab964f1ea77e7cfb7d801caa98e3","observation_id":"44dba42c-4e4a-4f33-a041-1fdb4b8f7a8d","resolution":{"observed_at":"2026-08-07T11:30:51.106031Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:51.161281Z","title":"Eeg signal analysis: a survey,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:51.161281Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:82ac242f08df976058b0c6e392bd53b9a0387595d376418affe887a8393f4a4c","observation_id":"9e84ec41-0a03-43dc-8035-5644f1a5bcb9","resolution":{"observed_at":"2026-08-07T11:30:51.161281Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:51.249327Z","title":"Analysis of electroencephalography (eeg) signals and its categorization–a study,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:51.249327Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:1a6527b60558c41cb7bcd47e1a52d73e1abc49066153deff0153dd659d087467","observation_id":"219c2895-477e-46ce-9307-2ed9d052cf3d","resolution":{"observed_at":"2026-08-07T11:30:51.249327Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:51.313168Z","title":"Eeg signal analysis: a survey,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:51.313168Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:bff3d2fc7afdd58fc9968699b572c0191bbc922c524728c99a7f712b56980499","observation_id":"db342b92-81be-438f-b3f0-7abb82fbeff1","resolution":{"observed_at":"2026-08-07T11:30:51.313168Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2410.07507","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:31:01.729114Z","title":"Thought2text: Text generation from eeg signal using large language models (llms),","venue":null,"work_id":"65fd8168-fdab-418b-b4a0-bedef173b569","year":2024},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:51.390927Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:c21cd674f9be35e214c2bbbfdbde608d35a5d82f80862716502b381736c1e282","observation_id":"d17b0f67-7046-4b07-89ad-36c5475d38e1","resolution":{"observed_at":"2026-08-07T11:31:01.733628Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:51.492362Z","title":"Hidden states in llms improve eeg representation learning and visual decoding,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:51.492362Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:67653b058747c72f8b9b49a5cf2fdbede4811b335412d06dd0eadc38ba458bfd","observation_id":"85e64ec7-8fee-4bf6-9cc4-2421746de257","resolution":{"observed_at":"2026-08-07T11:30:51.492362Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.11936","last_updated":"2025-05-05T13:22:05Z","snapshot_observed_at":"2026-08-07T16:01:52.009427Z","submitted_at":"2025-04-16T10:16:03Z","title":"Mind2Matter: Creating 3D Models from EEG Signals","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.11936","snapshot_observed_at":"2026-08-07T11:30:51.578678Z","title":"Mind2matter: Creating 3d models from eeg signals,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:51.578678Z"},"links":{"cited_paper":"/paper/2504.11936","citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:6f5c84e37a4987b1cbac94c38c23556fbe258a710ff9def69d05a4aff83c8634","observation_id":"a88fd05b-8e6e-4bb3-b300-bbdbca23f368","resolution":{"observed_at":"2026-08-07T11:30:51.578678Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:51.648282Z","title":"Exploring large-scale language models to evaluate eeg-based multimodal data for mental health,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:51.648282Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:c3f68a6ca6a38a16b637280506a531dbf92438ec54a982f0650bc92084b87259","observation_id":"3620d2c1-6a22-4742-9118-65dc1a95caf9","resolution":{"observed_at":"2026-08-07T11:30:51.648282Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:51.718374Z","title":"Eeg artifact removal—state-of-the-art and guidelines,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:51.718374Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:240f69d736d29b786d271375d01e7cf535acc406dabf9cf6aec62b27602cca34","observation_id":"71db3979-a17d-48f9-9649-878953ec831a","resolution":{"observed_at":"2026-08-07T11:30:51.718374Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:51.778922Z","title":"Identification and removal of physiological artifacts from electroencephalogram signals: A review,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:51.778922Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:b3c15f2d4f1f9c345fd96c2ddd8150277d5262fb765be5962ff39639b2e02b6e","observation_id":"1941c4cf-3c7f-43f1-87e7-37d1bc6dc4d6","resolution":{"observed_at":"2026-08-07T11:30:51.778922Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:51.875511Z","title":"Removal of artifacts from eeg signals: A review,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:51.875511Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:924bb63c3faaf549e59b35eac4548ad932454a52c16554f4337ff4034f0d0d51","observation_id":"75c2a625-899a-4cfa-81c3-987fb82e8284","resolution":{"observed_at":"2026-08-07T11:30:51.875511Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2023.11226","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:31:01.663191Z","title":"Discrepancy between inter- and intra-subject variability in eeg-based motor imagery brain-computer interface: Evidence from multiple perspectives,","venue":null,"work_id":"154ea175-7f90-45fc-9fe8-94e145692be9","year":2023},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:51.959588Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:eac4a5d90e2bd0908755659e82cae10b320dda8a4d42f6077111d4a1ba77e42f","observation_id":"c16df790-0c74-4b61-bb1d-3484a976514c","resolution":{"observed_at":"2026-08-07T11:31:01.667869Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:52.041743Z","title":"Intra- and inter-subject variability in eeg-based sensorimotor brain computer interface: A review,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:52.041743Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:3158f18f1584727d0a601095a1ff9a83d116c01c78d2b4b1a8c4973c88cf1dec","observation_id":"43df875d-c71f-4ba8-b119-312a87279913","resolution":{"observed_at":"2026-08-07T11:30:52.041743Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:52.191003Z","title":"A survey on evaluation of large language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:52.191003Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:984239dcd682654ac557064eff41ebb6a22e6325c3a2deb0fe42c0d146f4941d","observation_id":"5435962f-9e30-42ea-8592-7a3620fa8f8d","resolution":{"observed_at":"2026-08-07T11:30:52.191003Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:52.274878Z","title":"Attention is all you need,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:52.274878Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:20da0b7141be95e4ff5fef8a99016216a692eff0c229ab59e286744339e7c5f5","observation_id":"b51513c5-c167-41f7-a7d8-4d16a0d2039e","resolution":{"observed_at":"2026-08-07T11:30:52.274878Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.15025","last_updated":"2023-09-26T15:49:23Z","snapshot_observed_at":"2026-07-06T16:23:53.247204Z","submitted_at":"2023-09-26T15:49:23Z","title":"Large Language Model Alignment: A Survey","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.15025","snapshot_observed_at":"2026-08-07T11:30:52.361596Z","title":"Large lan- guage model alignment: A survey,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:52.361596Z"},"links":{"cited_paper":"/paper/2309.15025","citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:ebd33da6496bb012e35a2dbbd54740a2cf83767fb750a4a1a729e0ff4bacc192","observation_id":"45cac6e4-bee0-4ba4-a9e6-48d0e5f62e56","resolution":{"observed_at":"2026-08-07T11:30:52.361596Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.12966","last_updated":"2023-07-24T17:44:58Z","snapshot_observed_at":"2026-07-06T15:57:57.167169Z","submitted_at":"2023-07-24T17:44:58Z","title":"Aligning Large Language Models with Human: A Survey","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.12966","snapshot_observed_at":"2026-08-07T11:30:52.449611Z","title":"Aligning large lan- guage models with human: A survey,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:52.449611Z"},"links":{"cited_paper":"/paper/2307.12966","citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:65561ea66bcee1d4893382229203e8198d91acb8b57694c18ec872c301bd056e","observation_id":"40e765d3-f022-43db-b21d-72a3bceeaf90","resolution":{"observed_at":"2026-08-07T11:30:52.449611Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2023.11488","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:31:01.539436Z","title":"Eegformer: A transformer–based brain activity classification method using eeg signal,","venue":null,"work_id":"5b7a40e4-1d73-4824-87c6-7daf73609625","year":2023},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:52.533850Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:a8a6694b39197b33e1f8000943cdd3d9f3963dd1f27a7b48ea585fe2a49cf40e","observation_id":"505c78aa-2e55-41b5-9e45-3ff22d3b3476","resolution":{"observed_at":"2026-08-07T11:31:01.544379Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:52.594083Z","title":"Transformers for eeg-based emotion recognition: A hierarchical spatial information learning model,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:52.594083Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:038150e5795dc1621f7f4e66a31228716d3a1a0c8a1b59e9c05f72fa24d52619","observation_id":"5185c244-0e2e-4fef-a732-4c3fc4baa69b","resolution":{"observed_at":"2026-08-07T11:30:52.594083Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.02165","last_updated":"2024-05-03T15:14:19Z","snapshot_observed_at":"2026-08-04T03:00:56.656285Z","submitted_at":"2024-05-03T15:14:19Z","title":"EEG2TEXT: Open Vocabulary EEG-to-Text Decoding with EEG Pre-Training and Multi-View Transformer","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.02165","snapshot_observed_at":"2026-08-07T11:30:52.681329Z","title":"Eeg2text: Open vocabulary eeg-to-text de- coding with eeg pre-training and multi-view trans- former,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:52.681329Z"},"links":{"cited_paper":"/paper/2405.02165","citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:0001783b8411ba442cbd6ec441c3b8ef4b21d62a14f584c511dbc257e13a7790","observation_id":"f1e418e0-fadd-4934-8306-5d50516db477","resolution":{"observed_at":"2026-08-07T11:30:52.681329Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:52.753109Z","title":"Brain–computer interfaces for com- munication and rehabilitation,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:52.753109Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:1e0480a7248ef7fe28fbc9fb4a6ae449161cdaf7ff806b8f5bb759e20507f304","observation_id":"ddf69952-e992-412e-9ecd-71fc57d387b6","resolution":{"observed_at":"2026-08-07T11:30:52.753109Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:52.812251Z","title":"Electroen- cephalographic motor imagery brain connectivity analysis for bci: A review,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:52.812251Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:757f29df833336fec02df29ecd7d48cb21d1b658d987b67d5c3fa8d32aca4a95","observation_id":"a4526ebe-dd6d-4975-91cb-3872694d856d","resolution":{"observed_at":"2026-08-07T11:30:52.812251Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:52.878236Z","title":"Performance variation in motor imagery brain–computer interface: A brief review,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:52.878236Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:85b8a7dc5ea0a829519d5018eaa9bfbde342eef68d04ba11ff95a85a46809bf4","observation_id":"eb143223-cc24-47eb-8d68-941d444f2a7a","resolution":{"observed_at":"2026-08-07T11:30:52.878236Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:52.994757Z","title":"Steady-state visually evoked potentials: Focus on essential paradigms and future perspectives,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:52.994757Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:b5468474b5ec897cfa8c165e627bede44ef6184620784f70357d99476e97d54f","observation_id":"48223a1d-3ddc-4f1e-85ca-eef06206ba7d","resolution":{"observed_at":"2026-08-07T11:30:52.994757Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1088/1741-2560/2/4/008","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:31:00.682588Z","title":"Steady-state visual evoked potential (ssvep)-based communication: impact of harmonic frequency components,","venue":null,"work_id":"254cbff0-8128-43fd-b047-ccfad2f90efa","year":2005},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:53.061393Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:a9e82796ec8904986862708ca7cde2089dcff86afb83408d84dfb5cb414bf18a","observation_id":"a7c118f6-1d9d-48a9-90eb-70d6be29764a","resolution":{"observed_at":"2026-08-07T11:31:00.685344Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:53.118485Z","title":"A survey of multimodel large language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:53.118485Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:42c9e3f03d26b2d3013fbd7b672c753a7f9e9338209423e7911f8e6f310ddb15","observation_id":"40f6ac89-3349-4f78-9987-7f8891b17a60","resolution":{"observed_at":"2026-08-07T11:30:53.118485Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1802.05751","last_updated":"2018-06-15T23:27:07Z","snapshot_observed_at":"2026-07-06T06:23:45.215820Z","submitted_at":"2018-02-15T20:37:15Z","title":"Image Transformer","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.05751","snapshot_observed_at":"2026-08-07T11:30:53.216227Z","title":"Image transformer,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:53.216227Z"},"links":{"cited_paper":"/paper/1802.05751","citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:8c90461615daa455cb46cfc02c54c2e59ecc7b94b2e96e3c95ef34de057b2fe4","observation_id":"63dcb30f-0f5f-4993-8a71-6dffac2af960","resolution":{"observed_at":"2026-08-07T11:30:53.216227Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:53.287754Z","title":"Pre-trained models: Past, present and future,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:53.287754Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:e10075009ac099dd25b5dc470b5187bdab13e85ba74710c4f86b6bd54f3d8c86","observation_id":"e11aded1-9095-43fe-aeab-e02302a2d9c6","resolution":{"observed_at":"2026-08-07T11:30:53.287754Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.04805","last_updated":"2019-05-24T20:37:26Z","snapshot_observed_at":"2026-07-30T09:12:38.100527Z","submitted_at":"2018-10-11T00:50:01Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.04805","snapshot_observed_at":"2026-08-07T11:30:53.379696Z","title":"BERT: pre-training of deep bidirectional transformers for language understanding,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:53.379696Z"},"links":{"cited_paper":"/paper/1810.04805","citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:cb34684ceff6984e10521679bcbad0425f9d8e3b61d29d04788247c65b40ecaf","observation_id":"50990b2e-b136-46a2-8f26-662100d63b96","resolution":{"observed_at":"2026-08-07T11:30:53.379696Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2204.14198","last_updated":"2022-11-15T23:07:37Z","snapshot_observed_at":"2026-07-06T13:05:12.350238Z","submitted_at":"2022-04-29T16:29:01Z","title":"Flamingo: a Visual Language Model for Few-Shot Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.14198","snapshot_observed_at":"2026-08-07T11:30:53.496195Z","title":"Flamingo: a visual lan- guage model for few-shot learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:53.496195Z"},"links":{"cited_paper":"/paper/2204.14198","citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:5cd60714ec733939cde21d9c95b462ac533d38e852199694de068f422a389bc5","observation_id":"f4858d70-7495-440f-a5b2-17793a48ed6a","resolution":{"observed_at":"2026-08-07T11:30:53.496195Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.08485","last_updated":"2023-12-11T17:46:14Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-04-17T17:59:25Z","title":"Visual Instruction Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.08485","snapshot_observed_at":"2026-08-07T11:30:53.585830Z","title":"Visual instruction tuning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:53.585830Z"},"links":{"cited_paper":"/paper/2304.08485","citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:f55261cba5c00a344f6550ab07154773365a768c33a459f0fa57ee6ba6b4d75c","observation_id":"b72da765-582e-4993-ac89-bf245c6cfde2","resolution":{"observed_at":"2026-08-07T11:30:53.585830Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2109.01652","last_updated":"2022-02-08T20:26:45Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2021-09-03T17:55:52Z","title":"Finetuned Language Models Are Zero-Shot Learners","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.01652","snapshot_observed_at":"2026-08-07T11:30:53.692371Z","title":"Fine- tuned language models are zero-shot learners,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:53.692371Z"},"links":{"cited_paper":"/paper/2109.01652","citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:df1b282e1da7e0f4629b58a414f5fe2e6829258a62c971c98a20cfed399bd62f","observation_id":"2651b601-b89a-4bc4-a9b8-21d7442d2d08","resolution":{"observed_at":"2026-08-07T11:30:53.692371Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:53.768880Z","title":"Instruction tuning for large language models: A survey,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:53.768880Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:22307cb0552ebe4bf485d74b0c2166b7762cdadb8bef92656f85a4cc4541b780","observation_id":"1a4a6bf9-cab9-4652-86c0-5b85ec42628e","resolution":{"observed_at":"2026-08-07T11:30:53.768880Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.11654","last_updated":"2024-04-13T05:11:03Z","snapshot_observed_at":"2026-08-06T11:45:33.826699Z","submitted_at":"2023-08-20T12:54:17Z","title":"Large Transformers are Better EEG Learners","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.11654","snapshot_observed_at":"2026-08-07T11:30:53.873700Z","title":"Large transformers are better eeg learn- ers,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:53.873700Z"},"links":{"cited_paper":"/paper/2308.11654","citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:0ac77348db4a336d3cdffb51ee4ecaaf16515e233ebc50846019fcc37f4e679a","observation_id":"0c8c24ab-0cf9-47ec-b79c-e82b4458141c","resolution":{"observed_at":"2026-08-07T11:30:53.873700Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.00121","last_updated":"2024-08-28T12:30:22Z","snapshot_observed_at":"2026-08-06T09:55:56.764687Z","submitted_at":"2024-08-28T12:30:22Z","title":"BELT-2: Bootstrapping EEG-to-Language representation alignment for multi-task brain decoding","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.00121","snapshot_observed_at":"2026-08-07T11:30:53.948000Z","title":"Belt-2: Bootstrapping eeg-to-language repre- sentation alignment for multi-task brain decoding,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:53.948000Z"},"links":{"cited_paper":"/paper/2409.00121","citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:4ab0377816b22984eb0136ee988e0910ebb6adc6c743f23e210c051b4751b271","observation_id":"d38505c9-48fc-48b1-b1af-da9cf33727a8","resolution":{"observed_at":"2026-08-07T11:30:53.948000Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:54.029673Z","title":"A survey of zero-shot learning: Settings, methods, and applications,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:54.029673Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:ba6dde02b4ff28d66062f8d37f99a965712a287871b6fe9e0066900c17746f0d","observation_id":"0d558ce4-fd8d-4c49-8fe7-8b790f9c934d","resolution":{"observed_at":"2026-08-07T11:30:54.029673Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:54.136233Z","title":"A review of generalized zero-shot learning methods,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:54.136233Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:50c1ab049a4290be6dde0ec8c2ac173ded140c18565316ebc2c9b23612182f29","observation_id":"3b237294-fa00-4e34-8d6d-e9346f191717","resolution":{"observed_at":"2026-08-07T11:30:54.136233Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.16531","last_updated":"2025-07-29T14:09:40Z","snapshot_observed_at":"2026-08-07T16:55:12.755343Z","submitted_at":"2025-03-18T11:12:15Z","title":"EEG-CLIP : Learning EEG representations from natural language descriptions","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.16531","snapshot_observed_at":"2026-08-07T11:30:54.208261Z","title":"Eeg- clip : Learning eeg representations from natural language descriptions,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:54.208261Z"},"links":{"cited_paper":"/paper/2503.16531","citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:d23b74f00cf6e36d0adfb1c1548be3f48c26e3b649e5de3270a6ec6a5e9cb40f","observation_id":"48792131-49c1-4902-b16c-59e8bd08a134","resolution":{"observed_at":"2026-08-07T11:30:54.208261Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.08150","last_updated":"2024-09-05T02:21:05Z","snapshot_observed_at":"2026-07-06T18:44:37.726622Z","submitted_at":"2024-07-11T03:00:26Z","title":"Hypergraph Multi-modal Large Language Model: Exploiting EEG and Eye-tracking Modalities to Evaluate Heterogeneous Responses for Video Understanding","version":3},"cited_work":{"arxiv_id":"2407.08150","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.08150","snapshot_observed_at":"2026-08-07T11:31:01.263876Z","title":"Hypergraph Multi-modal Large Language Model: Exploiting EEG and Eye-tracking Modalities to Evaluate Heterogeneous Responses for Video Understanding","venue":"cs.CV","work_id":"2bf8ca6d-bda2-449f-8bcd-3bb2e02d7472","year":2024},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:54.296285Z"},"links":{"cited_paper":"/paper/2407.08150","citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:6d1250c2d427c8d0dcc751f4173cd05b3d695ad3228e04aa08775dbe5a04ae8a","observation_id":"5b20fe38-9473-4017-b70a-d0a89d364654","resolution":{"observed_at":"2026-08-07T11:31:01.267686Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05046","last_updated":"2020-03-29T16:47:41Z","snapshot_observed_at":"2026-07-06T07:45:10.078720Z","submitted_at":"2019-04-10T08:05:48Z","title":"Generalizing from a Few Examples: A Survey on Few-Shot Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.05046","snapshot_observed_at":"2026-08-07T11:30:54.385694Z","title":"Generalizing from a few examples: A survey on few-shot learning,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:54.385694Z"},"links":{"cited_paper":"/paper/1904.05046","citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:4bb53c5b10aa77822df1b0b5a538da9eeca095bcc8d50128a0f29b5394f69ab6","observation_id":"6dfc0b9b-7356-48b2-9862-e288a863018e","resolution":{"observed_at":"2026-08-07T11:30:54.385694Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.06743","last_updated":"2022-05-24T05:24:48Z","snapshot_observed_at":"2026-07-06T13:09:43.029055Z","submitted_at":"2022-05-13T16:24:35Z","title":"A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.06743","snapshot_observed_at":"2026-08-07T11:30:54.471258Z","title":"A comprehensive survey of few-shot learning: Evolution, applications, challenges, and opportunities,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:54.471258Z"},"links":{"cited_paper":"/paper/2205.06743","citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:7f85c1b7e1fec032982540be4f13544622d2deeadb090efcde14258d462178d2","observation_id":"6a31f292-faeb-4fb3-ab69-0c29411aa423","resolution":{"observed_at":"2026-08-07T11:30:54.471258Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.00166","last_updated":"2025-07-15T13:11:26Z","snapshot_observed_at":"2026-07-06T19:24:52.534713Z","submitted_at":"2024-09-30T19:15:05Z","title":"EEG Emotion Copilot: Optimizing Lightweight LLMs for Emotional EEG Interpretation with Assisted Medical Record Generation","version":3},"cited_work":{"arxiv_id":"2410.00166","doi":null,"metadata_source":"pith","pith_arxiv_id":"2410.00166","snapshot_observed_at":"2026-08-07T11:31:01.235307Z","title":"EEG Emotion Copilot: Optimizing Lightweight LLMs for Emotional EEG Interpretation with Assisted Medical Record Generation","venue":"cs.CV","work_id":"dfbe9de5-d44f-4b2a-8062-f36b632cfdda","year":2024},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:54.546828Z"},"links":{"cited_paper":"/paper/2410.00166","citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:e3bab34fab8ac105e02a1ee6c105688c91ba3a3fddd250c0ae67fccef2f181c1","observation_id":"23b1cd37-a91c-46ec-a85b-905943aa8b97","resolution":{"observed_at":"2026-08-07T11:31:01.239241Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:54.642866Z","title":"Advancing semi-supervised eeg emotion recognition through feature extraction with mixup and large language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:54.642866Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:c0740173aec9268268916346567de4bf768b278e606f98abe3b5ed4b83693206","observation_id":"afa1a612-5ba7-4941-99b2-47d29c049ae1","resolution":{"observed_at":"2026-08-07T11:30:54.642866Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:54.734012Z","title":"Classification of non-invasive eeg signals during motor imagery tasks using a large language model,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:54.734012Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:ab94261729cbc45cdbfbb3e910ba8e8986a164adbb42733a3cc75144422c84d1","observation_id":"257cbad3-0b04-4f07-a6a9-cb506a92d1a1","resolution":{"observed_at":"2026-08-07T11:30:54.734012Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2024.34160","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:31:01.220630Z","title":"Deep representation learning for open vocabulary electroencephalography-to-text decod- ing,","venue":null,"work_id":"ae6c8e01-e3a4-45d1-be27-4ef374b20a04","year":2024},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:54.808951Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:1b845f6bdcc3fea5d1a9c46732f37c79c7e460f149a57f6817444414f4b80aa4","observation_id":"5c891c4b-295f-4d56-bad9-9a0e65da5622","resolution":{"observed_at":"2026-08-07T11:31:01.225813Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2309.14030","last_updated":"2024-01-03T08:14:00Z","snapshot_observed_at":"2026-08-05T00:48:09.931353Z","submitted_at":"2023-09-25T10:52:28Z","title":"DeWave: Discrete EEG Waves Encoding for Brain Dynamics to Text Translation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.14030","snapshot_observed_at":"2026-08-07T11:30:54.901203Z","title":"Dewave: Discrete eeg waves encoding for brain dynamics to text translation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:54.901203Z"},"links":{"cited_paper":"/paper/2309.14030","citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:40101884f61cacbc0977e8697cebaae155d4e40b327086ab5b26178aed3e07b4","observation_id":"8369882d-a82f-4022-899f-c6dec3b2f4c2","resolution":{"observed_at":"2026-08-07T11:30:54.901203Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.18006","last_updated":"2024-02-03T23:32:08Z","snapshot_observed_at":"2026-08-03T13:13:35.978462Z","submitted_at":"2024-01-31T17:08:34Z","title":"EEG-GPT: Exploring Capabilities of Large Language Models for EEG Classification and Interpretation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.18006","snapshot_observed_at":"2026-08-07T11:30:54.977212Z","title":"Eeg-gpt: exploring capabilities of large language models for eeg classification and interpretation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:54.977212Z"},"links":{"cited_paper":"/paper/2401.18006","citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:fc4e6d47c922f2b2890de530f8c736fbad0b653df28867b9ba5586ff85fcba89","observation_id":"cea70ed8-eff0-4f3a-ba42-2a6efe7ee070","resolution":{"observed_at":"2026-08-07T11:30:54.977212Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.07480","last_updated":"2025-08-11T12:47:56Z","snapshot_observed_at":"2026-07-06T19:13:55.363649Z","submitted_at":"2024-09-02T10:03:03Z","title":"EEG-Language Pretraining for Highly Label-Efficient Clinical Phenotyping","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.07480","snapshot_observed_at":"2026-08-07T11:30:55.057703Z","title":"Eeg-language mod- eling for pathology detection,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:55.057703Z"},"links":{"cited_paper":"/paper/2409.07480","citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:2fb99dfd6c5de60745b61f673fb07ee333c27cde2d1837db499e1ceb5470f170","observation_id":"d927621e-721c-46cd-ab3f-9f0593c5220a","resolution":{"observed_at":"2026-08-07T11:30:55.057703Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:55.150840Z","title":"Eegunity: Open-source tool in facil- itating unified eeg datasets toward large-scale eeg model,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:55.150840Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:37f89649159e32e8ed13ed2ac6ef1233a49cc91d885a6f1416cc6bcf081bc6f3","observation_id":"71f29f31-754c-4b45-8bb6-cd22d1389273","resolution":{"observed_at":"2026-08-07T11:30:55.150840Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17433","last_updated":"2024-06-10T09:51:50Z","snapshot_observed_at":"2026-08-06T04:09:06.351450Z","submitted_at":"2024-02-27T11:45:21Z","title":"Enhancing EEG-to-Text Decoding through Transferable Representations from Pre-trained Contrastive EEG-Text Masked Autoencoder","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17433","snapshot_observed_at":"2026-08-07T11:30:55.238502Z","title":"Enhancing eeg-to-text decoding through transferable representations from pre-trained con- 17 trastive eeg-text masked autoencoder,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:55.238502Z"},"links":{"cited_paper":"/paper/2402.17433","citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:b674744904c8ed38691b9880a7dd42be2cae077dcdb69bf6d7f6ccd5f3760d74","observation_id":"a82d1c00-b898-4655-b042-22ee72e24367","resolution":{"observed_at":"2026-08-07T11:30:55.238502Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:55.306595Z","title":"Enhancing neural decoding with large language models: A gpt-based approach,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:55.306595Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:53754b70cf3e5d92e5bdefdacc22e5c38e925db46fe6f42e5c990a481bddc2fa","observation_id":"33dd52c4-7179-4f1f-b19b-23f297765a6f","resolution":{"observed_at":"2026-08-07T11:30:55.306595Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.07822","last_updated":"2026-06-20T02:35:56Z","snapshot_observed_at":"2026-08-06T16:39:55.092833Z","submitted_at":"2024-08-01T15:17:54Z","title":"Exploration of LLMs, EEG, and behavioral data to measure and support attention and sleep","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.07822","snapshot_observed_at":"2026-08-07T11:30:55.402126Z","title":"Explo- ration of llms, eeg, and behavioral data to mea- sure and support attention and sleep,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:55.402126Z"},"links":{"cited_paper":"/paper/2408.07822","citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:2f7dd23ed835c7c936b06fe09115b845da8fa2419e170f6478c07c10c03b0de2","observation_id":"90c894b0-d015-4484-b3a4-abd7349aa01e","resolution":{"observed_at":"2026-08-07T11:30:55.402126Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:55.493488Z","title":"Exploring the diagnostic potential of llms in schizophrenia detection through eeg analy- sis,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:55.493488Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:9cd69ff566bb1f1b53bd187c6f3d0f6180bb1fe9e98d2645df7a59477b2be7a9","observation_id":"1a9d9b67-a71a-439b-86cc-89f9bf10feba","resolution":{"observed_at":"2026-08-07T11:30:55.493488Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:55.573754Z","title":"From word embedding to reading embedding us- ing large language model, eeg and eye-tracking,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:55.573754Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:9da33d75aba6290f7383dae68c3e287a8c0d980c04fb99e8c2ebb7230e7cd89a","observation_id":"51aebd9a-fc8b-453c-95e8-f882eb6d8ca4","resolution":{"observed_at":"2026-08-07T11:30:55.573754Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.15714","last_updated":"2023-10-17T17:17:59Z","snapshot_observed_at":"2026-07-06T16:24:26.535120Z","submitted_at":"2023-09-27T15:12:08Z","title":"Integrating LLM, EEG, and Eye-Tracking Biomarker Analysis for Word-Level Neural State Classification in Semantic Inference Reading Comprehension","version":2},"cited_work":{"arxiv_id":"2309.15714","doi":null,"metadata_source":"pith","pith_arxiv_id":"2309.15714","snapshot_observed_at":"2026-08-07T11:31:01.119863Z","title":"Integrating LLM, EEG, and Eye-Tracking Biomarker Analysis for Word-Level Neural State Classification in Semantic Inference Reading Comprehension","venue":"cs.CL","work_id":"81266a93-3d4e-4a9b-bedc-29d4ae546863","year":2023},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:55.652466Z"},"links":{"cited_paper":"/paper/2309.15714","citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:fd71d07a302640d26c3819bb03d76f3760af69da09b2cd3684ba06587a77409f","observation_id":"8a3b223c-238c-49fb-9b1c-efa29695da5d","resolution":{"observed_at":"2026-08-07T11:31:01.123508Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.18765","last_updated":"2024-05-29T05:08:16Z","snapshot_observed_at":"2026-08-05T22:38:47.617492Z","submitted_at":"2024-05-29T05:08:16Z","title":"Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.18765","snapshot_observed_at":"2026-08-07T11:30:55.724716Z","title":"Large brain model for learning generic representations with tremendous eeg data in bci,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:55.724716Z"},"links":{"cited_paper":"/paper/2405.18765","citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:77258146facb907038aea201900234956d0313ef48cb5afe91c994251ecdb66c","observation_id":"3e71fba0-4a8c-4ee0-b22a-102353cae31a","resolution":{"observed_at":"2026-08-07T11:30:55.724716Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.17464","last_updated":"2025-02-11T04:28:10Z","snapshot_observed_at":"2026-07-06T20:41:51.646955Z","submitted_at":"2025-02-11T04:28:10Z","title":"Large Cognition Model: Towards Pretrained EEG Foundation Model","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.17464","snapshot_observed_at":"2026-08-07T11:30:55.819712Z","title":"Large cognition model: Towards pretrained eeg foundation model,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:55.819712Z"},"links":{"cited_paper":"/paper/2502.17464","citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:78831e7e447c2cb8d6e36ffd533572aae8d00ae2fa594d6ee32ef9cbcb0c5cd1","observation_id":"01d46bf9-a2de-4907-8626-c5a8b9b48d1d","resolution":{"observed_at":"2026-08-07T11:30:55.819712Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:55.880981Z","title":"Eegpt: Pretrained transformer for universal and reliable representation of eeg signals,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:55.880981Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:f2da9e305fcd4d1026ebe5d96561dd9783aaccdb157fa732387be332a6b29b6e","observation_id":"bf2d5f5a-32d2-4987-b0d8-d13a1f77fdcb","resolution":{"observed_at":"2026-08-07T11:30:55.880981Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:55.958325Z","title":"Leveraging large language models and fuzzy clus- tering for eeg report analysis,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:55.958325Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:f6a8ccafbc5de20609d1ddd8f4d73465b1117e8c7211053f1058f47e2d6b61ae","observation_id":"30e8adf6-7c38-4d3d-badb-0486d18d7928","resolution":{"observed_at":"2026-08-07T11:30:55.958325Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.03764","last_updated":"2024-03-02T07:06:39Z","snapshot_observed_at":"2026-08-03T12:38:58.403246Z","submitted_at":"2023-11-07T07:07:18Z","title":"Neuro-GPT: Towards A Foundation Model for EEG","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.03764","snapshot_observed_at":"2026-08-07T11:30:56.038635Z","title":"Neuro-gpt: Devel- oping a foundation model for eeg,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:56.038635Z"},"links":{"cited_paper":"/paper/2311.03764","citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:ded01adcd953f7db79e86cc09770f91b4e07f4319ca8d6273d37b1130fc47bd0","observation_id":"4d2c8432-9537-4f5e-befb-50b6cadc55f8","resolution":{"observed_at":"2026-08-07T11:30:56.038635Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:31:01.965144Z","title":"See: Semantically aligned eeg-to-text translation,","venue":null,"work_id":"ebe0bb13-eee6-4d8f-b84d-72cbec64ef7c","year":2025},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:56.116175Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:6d72c651c74b910bae35c9b067d919498a224d65c57d0e7b564fa2e845a3d933","observation_id":"8ec24332-9326-4765-9958-5b1b47e9a264","resolution":{"observed_at":"2026-08-07T11:31:01.968800Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.10278","last_updated":"2024-01-11T17:36:24Z","snapshot_observed_at":"2026-08-07T21:28:53.995779Z","submitted_at":"2024-01-11T17:36:24Z","title":"EEGFormer: Towards Transferable and Interpretable Large-Scale EEG Foundation Model","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.10278","snapshot_observed_at":"2026-08-07T11:30:56.190678Z","title":"Eegformer: Towards transferable and interpretable large-scale eeg foundation model,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:56.190678Z"},"links":{"cited_paper":"/paper/2401.10278","citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:5bd3e2eeceb99eed46da3bbff2ba32e0470db46a9bd433c831797687781be873","observation_id":"9b710374-b905-45e3-be07-ee2b571e9aa8","resolution":{"observed_at":"2026-08-07T11:30:56.190678Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:31:01.954983Z","title":"EEGTrans: Transformer- driven generative models for EEG synthesis,","venue":null,"work_id":"7085aa8a-b392-4e93-94c8-b14b932fe227","year":2025},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:56.285571Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:d7a96be6effe2472d1468eb511cda72f29565eca6a3c801cc74bb77d92f778f8","observation_id":"1feda5a5-3a1b-41ff-b7bc-81ad1c747ec0","resolution":{"observed_at":"2026-08-07T11:31:01.959367Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.19779","last_updated":"2025-08-29T08:30:30Z","snapshot_observed_at":"2026-08-04T21:40:18.880103Z","submitted_at":"2024-10-14T12:17:54Z","title":"BrainGPT: Unleashing the Potential of EEG Generalist Foundation Model by Autoregressive Pre-training","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.19779","snapshot_observed_at":"2026-08-07T11:30:56.367628Z","title":"Eegpt: Unleashing the potential of eeg generalist foundation model by autoregressive pre- training,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:56.367628Z"},"links":{"cited_paper":"/paper/2410.19779","citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:e4b2a63b29e7a10ec0968c1e3f3713a952a24f61661c913d7899680cbd5f1624","observation_id":"f2a7a9f5-6127-4e83-a7da-41b945448504","resolution":{"observed_at":"2026-08-07T11:30:56.367628Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:31:01.945045Z","title":"Investigating critical frequency bands and channels for eeg-based emotion recognition with deep neural networks,","venue":null,"work_id":"139e795f-0906-4ee6-80f0-b7ce24d780e9","year":2015},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:56.473022Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:9046b47429abe6637ba69993d2fb637b62abadfd41e25a1804744bb5c964662d","observation_id":"ef0ac3e6-a0a3-4f21-b1e7-00131c661bf6","resolution":{"observed_at":"2026-08-07T11:31:01.949133Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:31:01.935042Z","title":"Emotionmeter: A multimodal framework for recognizing human emotions,","venue":null,"work_id":"8200c1e3-8c01-4ca0-bee2-51dde32c911e","year":2019},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:56.552936Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:e45875ed1bc78281319efc9feb5a7962cb13a5c11ce47866f3eeacedbbc4cf95","observation_id":"e76d4505-002b-4a39-b88b-627bf4f98ff8","resolution":{"observed_at":"2026-08-07T11:31:01.938642Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:31:01.925269Z","title":"ZuCo 2.0: A dataset of physiological recordings during natural reading and annotation,","venue":null,"work_id":"583f5aae-20b4-4b6e-a252-b29fb30f6251","year":2020},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:56.610124Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:f8f528d88ce00cee91375521056142857971ff64d9cd2377edc99436ed88f3c1","observation_id":"378f8129-f9e8-4f3c-8a1d-3c57bd15d458","resolution":{"observed_at":"2026-08-07T11:31:01.929214Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:31:01.915586Z","title":"Deep learning human mind for automated visual classification,","venue":null,"work_id":"1c38c2e5-0bb5-4dc2-9ce8-27336a8e8bcf","year":2017},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:56.695537Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:0973b1c51a60feb006b8d084b4020ad0ea68751398f91f99ee8be778e7988071","observation_id":"27647172-8a1e-4f42-9bee-1ac4591d6037","resolution":{"observed_at":"2026-08-07T11:31:01.919274Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:31:01.905935Z","title":"Decoding brain represen- tations by multimodal learning of neural activity and visual features,","venue":null,"work_id":"a2f63532-366f-4d78-8615-ec84c81b52e2","year":2021},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:56.760430Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:c3ef7e12ed58aa48709ce1e89ec09e6ba38edc24eca28ec6051e6149b5966eed","observation_id":"eace6ef2-207e-4404-af38-289538ef427b","resolution":{"observed_at":"2026-08-07T11:31:01.909569Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:56.815734Z","title":"Analysis of eeg structural synchrony in adolescents with schizophrenic disorders,","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:56.815734Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:caacf017b9aedfb85dc0811b0bfd3fa7927b4fd39d8a447c85208f9a9ab04fc2","observation_id":"62d9ef69-23bd-431f-b60e-dea7840dba94","resolution":{"observed_at":"2026-08-07T11:30:56.815734Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:31:01.896982Z","title":"Analysis of a sleep-dependent neu- ronal feedback loop: the slow-wave microcontinuity of the eeg,","venue":null,"work_id":"c9309174-e97a-471e-8311-73a02fbd9e24","year":2000},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:56.885534Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:9d8e44d80bb22b06d45fcc39e427013cb5f8614b9137cb231ee3121819fe5969","observation_id":"ba0bf300-0221-42d2-a15b-584e4c8f6682","resolution":{"observed_at":"2026-08-07T11:31:01.900164Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:31:01.887734Z","title":"The bci competition iii: validating alternative approaches to actual bci problems,","venue":null,"work_id":"110c2aa9-7f70-40b3-bffb-f3dbc8c29206","year":2006},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:56.976796Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:d57d30738981aadbbb07d093f5f9735a5123a419112349c0a8c638977231d2a0","observation_id":"a5450822-890f-491d-bc7b-17c3f83da292","resolution":{"observed_at":"2026-08-07T11:31:01.891161Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:57.047236Z","title":"The temple university hospital eeg data corpus,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:57.047236Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:2eec5233bd6e48f50126718a1e9fadaf9378f2dd4eafcbff5eeec3a76c941371","observation_id":"b4ef40fe-1b64-46da-8887-15b67854a48a","resolution":{"observed_at":"2026-08-07T11:30:57.047236Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2015.74054","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:31:01.010277Z","title":"Automated identification of abnormal adult eegs,","venue":null,"work_id":"066a8d30-8d2a-40d3-9cbc-17e2753e7ce7","year":2015},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:57.118722Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:c30af9dc631490e7a235676dff061b1e28b14c08104c1c786e6b5906ee603f04","observation_id":"b908ba96-8868-4be4-9db5-131d2d175d49","resolution":{"observed_at":"2026-08-07T11:31:01.014648Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2021.75581","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:31:00.910684Z","title":"The nmt scalp eeg dataset: An open-source annotated dataset of healthy and pathological eeg recordings for predictive modeling,","venue":null,"work_id":"b79f0862-2632-47a5-8392-e272a20b9b16","year":2021},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:57.194756Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:1e3b7bc8e012a0aa3ab93a5c41c1aa8169bf6dc9d51e20c3de0c5b7b2e7bffda","observation_id":"668f6b86-60bd-4060-a38c-1be5957f5990","resolution":{"observed_at":"2026-08-07T11:31:00.919385Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:31:01.879009Z","title":"The temple university hospital seizure detec- tion corpus,","venue":null,"work_id":"40659943-362e-43e0-9f77-dc16d8521dc1","year":2018},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:57.288014Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:35a99dffc717f5f581788fcbdca8de2950f95bc59fa2277108ccd34036663cef","observation_id":"7ef23434-3051-465f-9101-74bc9a7109a0","resolution":{"observed_at":"2026-08-07T11:31:01.882178Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:31:01.869284Z","title":"Zuco, a simultaneous eeg and eye-tracking resource for natural sentence reading,","venue":null,"work_id":"f0765c74-bc74-4a58-b76c-3b4779010399","year":2018},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:57.349558Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:bffc3de90980a844437f7ca5435b496ebde6001a7a26ff318bd072fe4da1e704","observation_id":"6428d362-9f08-4c70-bf89-5a7157120580","resolution":{"observed_at":"2026-08-07T11:31:01.873026Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:31:01.859849Z","title":"Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals,","venue":null,"work_id":"47dcb723-9781-49e6-a969-7f3ea66aaf9a","year":2000},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:57.421515Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:f750893ffd7281c377133a137696bceca769d1f0dd55e804b4510500320aebb0","observation_id":"667c8362-2695-4c18-ab8c-ee74cc5881d1","resolution":{"observed_at":"2026-08-07T11:31:01.863123Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:31:01.851068Z","title":"A benchmark dataset for ssvep-based brain-computer interfaces,","venue":null,"work_id":"9703ce7b-33cf-4235-9d61-ec2cbb7bfe7e","year":2017},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:57.500197Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:bad1569163d4219659c9a8cd2c5cc8478848c7f9d659e460edacd613121211e3","observation_id":"19d0e218-6731-4f2d-b5ea-51a64321a3bf","resolution":{"observed_at":"2026-08-07T11:31:01.854450Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2012.00055","last_updated":"2020-11-30T19:14:00Z","snapshot_observed_at":"2026-08-02T03:40:47.017969Z","submitted_at":"2020-11-30T19:14:00Z","title":"Topological superconductivity in tripartite superconductor-ferromagnet-semiconductor nanowires","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2012.00055","snapshot_observed_at":"2026-08-07T11:30:57.571305Z","title":"Review of the bci competition iv,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:57.571305Z"},"links":{"cited_paper":"/paper/2012.00055","citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:4b31015510a2422b5e878f55f6a4d42cb6db93594197b3dd08c94acebaaef34c","observation_id":"b6fe7829-e03e-46e6-9a0c-2ecea873aeec","resolution":{"observed_at":"2026-08-07T11:30:57.571305Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1515/bmt-2014-0117","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:31:00.661502Z","title":"Random forests in non-invasive sensorimotor rhythm brain-computer interfaces: a practical and convenient non-linear classifier,","venue":null,"work_id":"0e08f04a-fd58-40c4-9fae-df42dad7a056","year":2016},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:57.657396Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:bfa984f492da511916167968437e9963cdcda3332994c03b07da065fbe974a18","observation_id":"32854dcd-c1ca-408f-8941-b89456d4c4ee","resolution":{"observed_at":"2026-08-07T11:31:00.665775Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:31:01.842509Z","title":"Deep learning with convolutional neural networks for eeg decoding and visualization,","venue":null,"work_id":"d4b61844-0fec-4129-bf0e-30b59bfadc29","year":null},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:57.749258Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:4f5672374ab4747356b6fa99ecaa17c055237922fcf1885f8e7d8ad3f7b88f7c","observation_id":"1a4d1f16-0346-4a4c-b9b8-23815dab400a","resolution":{"observed_at":"2026-08-07T11:31:01.845796Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:31:01.833175Z","title":"M3cv:a multi-subject, multi-session, and multi-task database for eeg- based biometrics challenge,","venue":null,"work_id":"6b9ffb95-6327-4a23-b2f5-17901958bd4d","year":2022},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:57.861163Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:a865aa27a33061ff4fff34cf50aaa1293a0bb9c506b59bb0233cc1fa9cd65bfa","observation_id":"e4b3e948-3e84-459e-b0c2-9ec2d081bb27","resolution":{"observed_at":"2026-08-07T11:31:01.836383Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:31:01.823840Z","title":"Objective and subjective evaluation of online error correction during p300-based spelling,","venue":null,"work_id":"36ce9e10-3580-43cd-a981-ffd9b3768e02","year":2012},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:57.937320Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:419620f5e3698d8dcae928cfc10f5f585cfa07c42936cad057803145ec0c2563","observation_id":"a8714ded-7411-49c0-b3a9-529c06745468","resolution":{"observed_at":"2026-08-07T11:31:01.827276Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:58.235888Z","title":"Physiobank, physiotoolkit, and physionet,","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:58.235888Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:4424235aed5235d8dc9e3b2b8111f580463bcd6cd1784fc4def74ca199385752","observation_id":"b047ebe4-c970-4510-bb30-f07233c98495","resolution":{"observed_at":"2026-08-07T11:30:58.235888Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:31:01.814696Z","title":"Deap: A database for emotion analysis ;using physiological signals,","venue":null,"work_id":"de070215-ec1e-4d27-afde-47a6bd0e2bef","year":2012},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:58.342667Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:952302a4c04aff451d62d391f783addf99f641ae37de863b742be5b5e9535db7","observation_id":"a6a5a189-687a-41ad-9175-c62c8535b828","resolution":{"observed_at":"2026-08-07T11:31:01.818304Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:58.473850Z","title":"A large finer-grained affective computing eeg dataset,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:58.473850Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:25adce2bcf3d93f25165519ba8f9bcd7b2752dcdad53848d4718aa4c06bea352","observation_id":"ecda143e-65dc-4a70-9865-569dfa52615c","resolution":{"observed_at":"2026-08-07T11:30:58.473850Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:31:01.805744Z","title":"Com- paring recognition performance and robustness of multimodal deep learning models for multimodal emotion recognition,","venue":null,"work_id":"d65233f5-9db3-4841-8e5b-ac62676a8554","year":2022},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:58.659322Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:a08446adc97438dfd62ef4af4ef20789e696c289d005a92c0dbc68157d05c7a4","observation_id":"c4679990-dcef-45b3-b14c-a5f991a459de","resolution":{"observed_at":"2026-08-07T11:31:01.809033Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:58.776087Z","title":"Eeg datasets for motor imagery brain–computer interface,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:58.776087Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:fcb794163802d5d9f867e7fc47c38ecff5916e9882c4ce07bc8ddac48c4fb1c0","observation_id":"db976b3d-d9c7-404a-8c78-2df9dfa5352f","resolution":{"observed_at":"2026-08-07T11:30:58.776087Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:31:01.795825Z","title":"Electroencephalo- grams during mental arithmetic task performance,","venue":null,"work_id":"79727242-f7d2-4c6c-9a92-df9c7077a8b0","year":2019},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:58.950437Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:32a9f218772a268d32974d406316162903f582b62f1aec46affb0bbf8eb684b8","observation_id":"6348163e-5f0a-41b6-a988-ca9cf76d9b50","resolution":{"observed_at":"2026-08-07T11:31:01.799908Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:31:01.786457Z","title":"Stew: Simul- taneous task eeg workload data set,","venue":null,"work_id":"4e0e2e61-781b-4734-ac9a-50c215cffe28","year":2018},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:59.096995Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:e914fa0a1513efe94e6c79219b2d7b7cb45664470a9c54f1a308095b68b00f1d","observation_id":"2d5061f0-4a9a-4cd8-ae70-30efcaaca09a","resolution":{"observed_at":"2026-08-07T11:31:01.789801Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:59.218976Z","title":"Haaglan- den medisch centrum sleep staging database (ver- sion 1.1),","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:59.218976Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:e95a64741801db1859445d23eec81a2cc2ebc37480d54fcb93ba929c168db916","observation_id":"c50358aa-1d37-47a2-90dc-c0d8c23f895c","resolution":{"observed_at":"2026-08-07T11:30:59.218976Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:59.346673Z","title":"Inferring imagined speech using eeg signals: a new approach using riemannian manifold features,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:59.346673Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:90e0c0e7d40bf67e14fd795ffa8277905036b9f928ef9c3e119aad7cd475636f","observation_id":"3e142242-1be9-4818-bfba-8f3fcff0adad","resolution":{"observed_at":"2026-08-07T11:30:59.346673Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:59.473736Z","title":"Generative adversarial networks: An overview,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:59.473736Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:5e04b2a3fa06c67a52f16dfa8bbb735dba08bf4d9139b513c1360f33ab24b172","observation_id":"15bde8a6-0ead-4c5e-b45d-2aa6686ab49e","resolution":{"observed_at":"2026-08-07T11:30:59.473736Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:31:01.777545Z","title":"Generative adversarial networks,","venue":null,"work_id":"c0d0db3b-7709-4d32-b069-642f8bdfc466","year":null},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:59.646527Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:e967dc01ae619199c16958c4c959f5bc4aead8321f6d2f25cabe214ef71d6858","observation_id":"9af6b6b9-a52d-494d-91a1-c9a445fee1e9","resolution":{"observed_at":"2026-08-07T11:31:01.780686Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:31:01.768527Z","title":"Recent progress on generative adversarial networks (gans): A survey,","venue":null,"work_id":"8f458ac7-91ae-4f3e-bc9f-2c0c832acf9c","year":2019},"citing_paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy","version":1},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:59.978628Z"},"links":{"citing_paper":"/paper/2506.06353"},"observation_digest":"sha256:034cd06e881b210f3a9cae5fa741cf41660cde3090a90e2107384d6587b7f84c","observation_id":"37cb464b-43e0-475a-821c-bdb246a6b795","resolution":{"observed_at":"2026-08-07T11:31:01.771764Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.06353","last_updated":"2025-06-02T18:58:57Z","latest_version":1,"primary_category":"eess.SP","snapshot_observed_at":"2026-08-07T21:30:14.270436Z","submitted_at":"2025-06-02T18:58:57Z","title":"Large Language Models for EEG: A Comprehensive Survey and Taxonomy"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":2,"metadata_mismatch":5,"parse_uncertain":0,"unresolved":65,"verified_exact":6,"verified_fuzzy":22},"total_outbound_references":107},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 100 of 107 outbound references and 0 inbound Pith citation observations for arXiv:2506.06353."}