{"as_of":"2026-08-19T06:23:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c860199a2681949808452eafcff9ad3bf5bfa24f8ff925976ccf78852ce2032c","coverage":[{"denominator":20,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":20,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T22:02:36.800670Z","state":"measured"},{"denominator":20,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":20,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+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/2506.22807/citation-record","integrity":"/paper/2506.22807/integrity","json":"/paper/2506.22807/citation-record.json","paper":"/paper/2506.22807"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.21154","last_updated":"2026-05-05T08:24:55Z","snapshot_observed_at":"2026-08-14T20:17:33.338190Z","submitted_at":"2025-02-28T15:32:01Z","title":"Hypergraph Multi-Modal Learning for EEG-based Emotion Recognition in Conversation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.21154","snapshot_observed_at":"2026-08-06T22:02:34.974269Z","title":"Hypergraph multi-modal learning for EEG-based emotion recognition in conversation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.22807","last_updated":"2025-08-19T03:57:12Z","snapshot_observed_at":"2026-08-14T20:18:17.087077Z","submitted_at":"2025-06-28T08:18:05Z","title":"FreqDGT: Frequency-Adaptive Dynamic Graph Networks with Transformer for Cross-subject EEG Emotion Recognition","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T22:02:34.974269Z"},"links":{"cited_paper":"/paper/2502.21154","citing_paper":"/paper/2506.22807"},"observation_digest":"sha256:cf23b1a5f5edc5d3277daeec6521d60a635f22eb59e1576d625d95c12cc7528a","observation_id":"5de9afb8-f2c3-435c-8ec4-4f3f2e346bba","resolution":{"observed_at":"2026-08-06T22:02:34.974269Z","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":"2026-08-06T22:02:39.756260Z","title":"EEG emotion recognition using dynamical graph convolutional neural networks","venue":null,"work_id":"b56dbab1-9700-41cb-a65e-ae50b1650109","year":2018},"citing_paper":{"arxiv_id":"2506.22807","last_updated":"2025-08-19T03:57:12Z","snapshot_observed_at":"2026-08-14T20:18:17.087077Z","submitted_at":"2025-06-28T08:18:05Z","title":"FreqDGT: Frequency-Adaptive Dynamic Graph Networks with Transformer for Cross-subject EEG Emotion Recognition","version":3},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T22:02:35.044886Z"},"links":{"citing_paper":"/paper/2506.22807"},"observation_digest":"sha256:7962c626f58d12f170d06283d9116b2ba407bed32c35083e0470643ad7c0a4f3","observation_id":"84b3671b-bdd5-4bec-b4d8-45c83332803a","resolution":{"observed_at":"2026-08-06T22:02:39.812075Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.18345","last_updated":"2025-03-17T02:22:04Z","snapshot_observed_at":"2026-08-17T15:33:53.800303Z","submitted_at":"2024-06-26T13:42:11Z","title":"EmT: A Novel Transformer for Generalized Cross-subject EEG Emotion Recognition","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.18345","snapshot_observed_at":"2026-08-06T22:02:35.111519Z","title":"EmT: A novel transformer for generalized cross-subject eeg emotion recognition","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.22807","last_updated":"2025-08-19T03:57:12Z","snapshot_observed_at":"2026-08-14T20:18:17.087077Z","submitted_at":"2025-06-28T08:18:05Z","title":"FreqDGT: Frequency-Adaptive Dynamic Graph Networks with Transformer for Cross-subject EEG Emotion Recognition","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T22:02:35.111519Z"},"links":{"cited_paper":"/paper/2406.18345","citing_paper":"/paper/2506.22807"},"observation_digest":"sha256:17d6621b184d4cd6e450fe44ca9b13fa93ab8b7a3983e0f22d6baf2e50de0de8","observation_id":"3565cc29-5a07-42fd-a2f4-cc5cfc7ea3c9","resolution":{"observed_at":"2026-08-06T22:02:35.111519Z","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":"2026-08-06T22:02:39.658335Z","title":"Approaches, applications, and challenges in physiological emotion recognition—a tutorial overview","venue":null,"work_id":"3f3f464e-2e8a-4990-a678-4b382c8c6abf","year":2023},"citing_paper":{"arxiv_id":"2506.22807","last_updated":"2025-08-19T03:57:12Z","snapshot_observed_at":"2026-08-14T20:18:17.087077Z","submitted_at":"2025-06-28T08:18:05Z","title":"FreqDGT: Frequency-Adaptive Dynamic Graph Networks with Transformer for Cross-subject EEG Emotion Recognition","version":3},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T22:02:35.177669Z"},"links":{"citing_paper":"/paper/2506.22807"},"observation_digest":"sha256:7a3f97ff88fd2f586dc2891c5bd73a4b7ecbe9dd44eb6561182d28c97552e218","observation_id":"083d812e-1600-4956-bebc-4298b8cdf36e","resolution":{"observed_at":"2026-08-06T22:02:39.716696Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T22:02:39.533012Z","title":"A dual-branch dynamic graph convolution based adaptive transformer feature fusion network for EEG emotion recognition","venue":null,"work_id":"181e2afc-9e4b-4924-920a-2c1580bf9ff8","year":2022},"citing_paper":{"arxiv_id":"2506.22807","last_updated":"2025-08-19T03:57:12Z","snapshot_observed_at":"2026-08-14T20:18:17.087077Z","submitted_at":"2025-06-28T08:18:05Z","title":"FreqDGT: Frequency-Adaptive Dynamic Graph Networks with Transformer for Cross-subject EEG Emotion Recognition","version":3},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T22:02:35.263009Z"},"links":{"citing_paper":"/paper/2506.22807"},"observation_digest":"sha256:fb6931e27198e21a9dcfcea81cf17d932ad6b16456b15dd60da54d2e6cec7075","observation_id":"eb63f183-9535-4599-9796-3762a3451b79","resolution":{"observed_at":"2026-08-06T22:02:39.578722Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T22:02:39.377823Z","title":"PGCN: Pyramidal graph convolutional network for EEG emotion recognition","venue":null,"work_id":"5a3ec683-b632-48f5-97e2-256391cf9280","year":2024},"citing_paper":{"arxiv_id":"2506.22807","last_updated":"2025-08-19T03:57:12Z","snapshot_observed_at":"2026-08-14T20:18:17.087077Z","submitted_at":"2025-06-28T08:18:05Z","title":"FreqDGT: Frequency-Adaptive Dynamic Graph Networks with Transformer for Cross-subject EEG Emotion Recognition","version":3},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T22:02:35.360316Z"},"links":{"citing_paper":"/paper/2506.22807"},"observation_digest":"sha256:c7e88a3ce3f7fff78a7405d054bd6d8a01ec80f06daed8ce251c8dd272071cad","observation_id":"67e8f051-24d7-4167-846d-50014a71020b","resolution":{"observed_at":"2026-08-06T22:02:39.428763Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2504.09156","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:02:37.024002Z","title":"LEREL: Lipschitz continuity-constrained emotion recognition ensemble learning for electroencephalography","venue":null,"work_id":"c459668f-81d1-4ec3-ab6a-35a184762223","year":2025},"citing_paper":{"arxiv_id":"2506.22807","last_updated":"2025-08-19T03:57:12Z","snapshot_observed_at":"2026-08-14T20:18:17.087077Z","submitted_at":"2025-06-28T08:18:05Z","title":"FreqDGT: Frequency-Adaptive Dynamic Graph Networks with Transformer for Cross-subject EEG Emotion Recognition","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T22:02:35.466568Z"},"links":{"citing_paper":"/paper/2506.22807"},"observation_digest":"sha256:f4e0719fd3db07e470bb1dacdf38ad0667893e1a1049819747fe617db6cd961d","observation_id":"7c9964c7-d569-4786-8d5c-2c1468b3c9fb","resolution":{"observed_at":"2026-08-06T22:02:37.103886Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T22:02:39.261051Z","title":"Dynamic domain adaptation for class-aware cross-subject and cross-session EEG emotion recognition","venue":null,"work_id":"2578949b-79d6-48ce-af8c-2cb33cce29ac","year":2022},"citing_paper":{"arxiv_id":"2506.22807","last_updated":"2025-08-19T03:57:12Z","snapshot_observed_at":"2026-08-14T20:18:17.087077Z","submitted_at":"2025-06-28T08:18:05Z","title":"FreqDGT: Frequency-Adaptive Dynamic Graph Networks with Transformer for Cross-subject EEG Emotion Recognition","version":3},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T22:02:35.557347Z"},"links":{"citing_paper":"/paper/2506.22807"},"observation_digest":"sha256:a9aeeaf56cf6f6d027fdac601e5955b105418ed4d8872bd4629199b48770cbff","observation_id":"d4b62762-ad4b-4679-b1dd-0c402b8e0dc7","resolution":{"observed_at":"2026-08-06T22:02:39.291184Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T22:02:39.135205Z","title":"EEG-based emotion recognition using regularized graph neural networks","venue":null,"work_id":"40e4fbd8-cf94-46df-bb08-8c7673427483","year":2020},"citing_paper":{"arxiv_id":"2506.22807","last_updated":"2025-08-19T03:57:12Z","snapshot_observed_at":"2026-08-14T20:18:17.087077Z","submitted_at":"2025-06-28T08:18:05Z","title":"FreqDGT: Frequency-Adaptive Dynamic Graph Networks with Transformer for Cross-subject EEG Emotion Recognition","version":3},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T22:02:35.661161Z"},"links":{"citing_paper":"/paper/2506.22807"},"observation_digest":"sha256:737c3d31f07ba73a4f9ea332cbc546676eda2b09c13567600912fdf74e18d2d5","observation_id":"939c3cb9-e5cd-41e8-aa65-ef80eb52e896","resolution":{"observed_at":"2026-08-06T22:02:39.175589Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T22:02:38.942639Z","title":"GCB-Net: Graph convolutional broad network and its application in emotion recognition","venue":null,"work_id":"7639d0a3-f477-4612-9f2b-5c041333bae0","year":2019},"citing_paper":{"arxiv_id":"2506.22807","last_updated":"2025-08-19T03:57:12Z","snapshot_observed_at":"2026-08-14T20:18:17.087077Z","submitted_at":"2025-06-28T08:18:05Z","title":"FreqDGT: Frequency-Adaptive Dynamic Graph Networks with Transformer for Cross-subject EEG Emotion Recognition","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T22:02:35.783216Z"},"links":{"citing_paper":"/paper/2506.22807"},"observation_digest":"sha256:0d91fe526f341dd447d06cac932d5ac4f8ecad16c8b04bff74325ea922e72947","observation_id":"cbd79e2c-3fab-473a-877f-c1bdd1bf3457","resolution":{"observed_at":"2026-08-06T22:02:39.037613Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T22:02:38.829181Z","title":"EEG Conformer: Convolutional transformer for eeg decoding and visualization","venue":null,"work_id":"98f5a202-0022-4971-ae6c-af63db0cf5d1","year":2022},"citing_paper":{"arxiv_id":"2506.22807","last_updated":"2025-08-19T03:57:12Z","snapshot_observed_at":"2026-08-14T20:18:17.087077Z","submitted_at":"2025-06-28T08:18:05Z","title":"FreqDGT: Frequency-Adaptive Dynamic Graph Networks with Transformer for Cross-subject EEG Emotion Recognition","version":3},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T22:02:35.859696Z"},"links":{"citing_paper":"/paper/2506.22807"},"observation_digest":"sha256:7360c5d606b6d3644ba195f8e248a60d9137f0e5321c8a33d0cd86a0d05eac9d","observation_id":"2cdf0a22-ecc0-4b4e-97d8-ad714ad4c780","resolution":{"observed_at":"2026-08-06T22:02:38.894214Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T22:02:38.695170Z","title":"AMDET: Attention based multiple dimensions EEG transformer for emotion recognition","venue":null,"work_id":"da98eb5d-71ab-4a9e-a267-dc8130719409","year":2023},"citing_paper":{"arxiv_id":"2506.22807","last_updated":"2025-08-19T03:57:12Z","snapshot_observed_at":"2026-08-14T20:18:17.087077Z","submitted_at":"2025-06-28T08:18:05Z","title":"FreqDGT: Frequency-Adaptive Dynamic Graph Networks with Transformer for Cross-subject EEG Emotion Recognition","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T22:02:35.943156Z"},"links":{"citing_paper":"/paper/2506.22807"},"observation_digest":"sha256:e8ec9bfd63e14714ddf3ec44e7fdb9dadc10a8b45f01517ed5827fcbb9486860","observation_id":"985f5e94-0419-4309-9120-d6d9ef0fa787","resolution":{"observed_at":"2026-08-06T22:02:38.760779Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T22:02:38.555069Z","title":"MS-MDA: Multisource marginal distribution adaptation for cross-subject and cross-session EEG emotion recognition","venue":null,"work_id":"f25c9b5a-9c9a-4be5-b95f-29afb08b63f9","year":2021},"citing_paper":{"arxiv_id":"2506.22807","last_updated":"2025-08-19T03:57:12Z","snapshot_observed_at":"2026-08-14T20:18:17.087077Z","submitted_at":"2025-06-28T08:18:05Z","title":"FreqDGT: Frequency-Adaptive Dynamic Graph Networks with Transformer for Cross-subject EEG Emotion Recognition","version":3},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T22:02:36.015882Z"},"links":{"citing_paper":"/paper/2506.22807"},"observation_digest":"sha256:8a13a4b9a9ef5734864d41e6c129de4e36739ed4f45990a457912aa5a943a7b9","observation_id":"446efa10-19b8-48c2-b25a-e930fb7fccda","resolution":{"observed_at":"2026-08-06T22:02:38.587357Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T22:02:38.444877Z","title":"Contrastive learning of subject-invariant EEG representations for cross-subject emotion recognition","venue":null,"work_id":"b57cb952-cee2-4b65-869e-ed32dfb0e797","year":2022},"citing_paper":{"arxiv_id":"2506.22807","last_updated":"2025-08-19T03:57:12Z","snapshot_observed_at":"2026-08-14T20:18:17.087077Z","submitted_at":"2025-06-28T08:18:05Z","title":"FreqDGT: Frequency-Adaptive Dynamic Graph Networks with Transformer for Cross-subject EEG Emotion Recognition","version":3},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T22:02:36.127435Z"},"links":{"citing_paper":"/paper/2506.22807"},"observation_digest":"sha256:9d104c194b17ed0027d11d9f8d4380a121fccd717e398b8af119ce485202788e","observation_id":"299c205e-dd3e-4710-9c5a-571de60e6015","resolution":{"observed_at":"2026-08-06T22:02:38.513084Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.17337","last_updated":"2025-03-22T06:52:48Z","snapshot_observed_at":"2026-08-17T05:27:36.313251Z","submitted_at":"2024-12-23T07:02:44Z","title":"Neural-MCRL: Neural Multimodal Contrastive Representation Learning for EEG-based Visual Decoding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.17337","snapshot_observed_at":"2026-08-06T22:02:36.175772Z","title":"Neural-MCRL: Neural multimodal contrastive representation learning for EEG-based visual decoding","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.22807","last_updated":"2025-08-19T03:57:12Z","snapshot_observed_at":"2026-08-14T20:18:17.087077Z","submitted_at":"2025-06-28T08:18:05Z","title":"FreqDGT: Frequency-Adaptive Dynamic Graph Networks with Transformer for Cross-subject EEG Emotion Recognition","version":3},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T22:02:36.175772Z"},"links":{"cited_paper":"/paper/2412.17337","citing_paper":"/paper/2506.22807"},"observation_digest":"sha256:544f05b4c4dc1541ea70c4d03f5a164727e6e11c877be01c10b00a5d70f073c8","observation_id":"2caa24a2-9391-4278-b787-6d0a607e081c","resolution":{"observed_at":"2026-08-06T22:02:36.175772Z","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":"2026-08-06T22:02:38.136784Z","title":"Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks","venue":null,"work_id":"d305c641-39ae-4319-8173-cc8919268ccc","year":2015},"citing_paper":{"arxiv_id":"2506.22807","last_updated":"2025-08-19T03:57:12Z","snapshot_observed_at":"2026-08-14T20:18:17.087077Z","submitted_at":"2025-06-28T08:18:05Z","title":"FreqDGT: Frequency-Adaptive Dynamic Graph Networks with Transformer for Cross-subject EEG Emotion Recognition","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T22:02:36.285837Z"},"links":{"citing_paper":"/paper/2506.22807"},"observation_digest":"sha256:e11f4f33f7be327455b5d2a803842bb7f53fb4896f4aea652814ec61c2a999c4","observation_id":"ade13a50-0658-4b02-bcdb-24046ac8d166","resolution":{"observed_at":"2026-08-06T22:02:38.283106Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T22:02:37.906706Z","title":"A large finer-grained affective computing EEG dataset.Scientific Data, 10(1):740, 2023","venue":null,"work_id":"a469c287-8922-4956-813d-4ef8d0d7f350","year":2023},"citing_paper":{"arxiv_id":"2506.22807","last_updated":"2025-08-19T03:57:12Z","snapshot_observed_at":"2026-08-14T20:18:17.087077Z","submitted_at":"2025-06-28T08:18:05Z","title":"FreqDGT: Frequency-Adaptive Dynamic Graph Networks with Transformer for Cross-subject EEG Emotion Recognition","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T22:02:36.350770Z"},"links":{"citing_paper":"/paper/2506.22807"},"observation_digest":"sha256:9acdb02a7eaf2f551fef76d30006ca5b54eef1a896f2848da9317d11035df3d4","observation_id":"96780325-53fa-4254-84ae-87c68c202ae1","resolution":{"observed_at":"2026-08-06T22:02:38.002378Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T22:02:37.693285Z","title":"Emotionmeter: A multimodal framework for recognizing human emotions","venue":null,"work_id":"02063b7d-51c4-4aa5-8b25-7132b85a1d72","year":2018},"citing_paper":{"arxiv_id":"2506.22807","last_updated":"2025-08-19T03:57:12Z","snapshot_observed_at":"2026-08-14T20:18:17.087077Z","submitted_at":"2025-06-28T08:18:05Z","title":"FreqDGT: Frequency-Adaptive Dynamic Graph Networks with Transformer for Cross-subject EEG Emotion Recognition","version":3},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T22:02:36.477700Z"},"links":{"citing_paper":"/paper/2506.22807"},"observation_digest":"sha256:31512bb456307d56179a3d141a796161711eaa72c9d6a2f11a69fcf429f48392","observation_id":"bc7fc3fd-f8b3-46d8-bcac-982de443af3e","resolution":{"observed_at":"2026-08-06T22:02:37.802563Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T22:02:37.493753Z","title":"EEG alpha activity reflects attentional demands, and beta activity reflects emotional and cognitive processes","venue":null,"work_id":"1bb77bb7-23ba-4d67-8cd6-5853751e1488","year":1985},"citing_paper":{"arxiv_id":"2506.22807","last_updated":"2025-08-19T03:57:12Z","snapshot_observed_at":"2026-08-14T20:18:17.087077Z","submitted_at":"2025-06-28T08:18:05Z","title":"FreqDGT: Frequency-Adaptive Dynamic Graph Networks with Transformer for Cross-subject EEG Emotion Recognition","version":3},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T22:02:36.646470Z"},"links":{"citing_paper":"/paper/2506.22807"},"observation_digest":"sha256:e9a00020c74b2d5e2c3fc58fc7920c77774304eaaa179b77f79216b9ea0364ce","observation_id":"1dbbcbc4-4d27-4b42-9135-7085a22088c0","resolution":{"observed_at":"2026-08-06T22:02:37.591461Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T22:02:37.274800Z","title":"On the role of asymmetric frontal cortical activity in approach and withdrawal motivation: An updated review of the evidence","venue":null,"work_id":"9845a076-5c5d-470e-b042-90c9843705d1","year":2018},"citing_paper":{"arxiv_id":"2506.22807","last_updated":"2025-08-19T03:57:12Z","snapshot_observed_at":"2026-08-14T20:18:17.087077Z","submitted_at":"2025-06-28T08:18:05Z","title":"FreqDGT: Frequency-Adaptive Dynamic Graph Networks with Transformer for Cross-subject EEG Emotion Recognition","version":3},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T22:02:36.800670Z"},"links":{"citing_paper":"/paper/2506.22807"},"observation_digest":"sha256:00e92d63145e2a44b9bb2b132cccdfcb9c68b8c6e2853e5badf9248fca38a152","observation_id":"02a3d8bd-f97c-4876-94b2-36a21805195b","resolution":{"observed_at":"2026-08-06T22:02:37.376966Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.22807","last_updated":"2025-08-19T03:57:12Z","latest_version":3,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-14T20:18:17.087077Z","submitted_at":"2025-06-28T08:18:05Z","title":"FreqDGT: Frequency-Adaptive Dynamic Graph Networks with Transformer for Cross-subject EEG Emotion Recognition"},"reference_resolution":{"displayed":20,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":3,"verified_exact":1,"verified_fuzzy":16},"total_outbound_references":20},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2506.22807."}