Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer | Paper Citation Record

Observation 5d5a5d3e-6ce3-42dc-8a25-a512769c28bd · outbound

This paper cites an unresolved cited work.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Unresolved cited work

Reference 1

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Observation ca64d5e1-d9a0-4f52-92b6-0f94851c550b · outbound

This paper cites Epilepsy in adults.The Lancet, 393(10172):689–701, 2019.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Epilepsy in adults.The Lancet, 393(10172):689–701, 2019

Reference 2

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Observation a849673d-be2a-4a2a-8e55-541e8e6ff660 · outbound

This paper cites The epidemiology of epilepsy.Neuroepidemiology, 54(2):185–191, 2020.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer The epidemiology of epilepsy.Neuroepidemiology, 54(2):185–191, 2020

Reference 3

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This paper cites Critical slowing down as a biomarker for seizure susceptibility.Nature Communications, 11(1):2172, 2020.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Critical slowing down as a biomarker for seizure susceptibility.Nature Communications, 11(1):2172, 2020

Reference 4

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Observation 77f3f2cb-6f69-4e09-ac7e-a96f3764b10e · outbound

This paper cites Noninvasive detection of hippocampal epileptiform activity on scalp electroencephalogram.JAMA Neurology, 79(6):614–622, 2022.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Noninvasive detection of hippocampal epileptiform activity on scalp electroencephalogram.JAMA Neurology, 79(6):614–622, 2022

Reference 5

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Observation e276be87-ef8e-419c-947c-bc85832b3495 · outbound

This paper cites Source-sink connectivity: A novel interictal EEG marker for seizure localization.Brain, 145(11):3901–3915, 2022.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Source-sink connectivity: A novel interictal EEG marker for seizure localization.Brain, 145(11):3901–3915, 2022

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:20:13.640666Z

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Observation 97fcb9dc-6210-4b68-b66a-458b4cd8386f · outbound

This paper cites Deep learning-based electroencephalography analysis: A systematic review.Journal of Neural Engineering, 16(5):051001, 2019.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Deep learning-based electroencephalography analysis: A systematic review.Journal of Neural Engineering, 16(5):051001, 2019

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:20:13.624279Z

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Observation 456224b9-dc8d-47cc-82e5-cf41a3e3d540 · outbound

This paper cites B2-ViT Net: Broad vision transformer network with broad attention for seizure prediction.IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer B2-ViT Net: Broad vision transformer network with broad attention for seizure prediction.IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:20:13.606893Z

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This paper cites an unresolved cited work.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-08-15T19:20:13.589921Z

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This paper cites vEpiNet: A multimodal interictal epileptiform discharge detection method based on video and electroencephalogram data.Neural Networks, 175:106319, 2024.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer vEpiNet: A multimodal interictal epileptiform discharge detection method based on video and electroencephalogram data.Neural Networks, 175:106319, 2024

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:20:13.572934Z

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This paper cites PyHFO: Lightweight deep learning-powered end-to-end high-frequency oscillations analysis application.Journal of Neural Engineering, 21(3):036023, 2024.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer PyHFO: Lightweight deep learning-powered end-to-end high-frequency oscillations analysis application.Journal of Neural Engineering, 21(3):036023, 2024

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:20:13.556192Z

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This paper cites an unresolved cited work.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-08-15T19:20:13.540469Z

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This paper cites Deep residual learning for image recognition.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Deep residual learning for image recognition

Reference 13

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This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T19:20:12.558716Z

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This paper cites Self-supervised representation learning from electroencephalography signals.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Self-supervised representation learning from electroencephalography signals

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:20:13.514547Z

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This paper cites an unresolved cited work.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Unresolved cited work

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-08-15T19:20:13.499512Z

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This paper cites BrainBERT: Self-supervised representation learning for intracranial recordings.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer BrainBERT: Self-supervised representation learning for intracranial recordings

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T19:20:12.572626Z

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This paper cites Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T19:20:12.577490Z

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This paper cites Vector quantization pretraining for EEG time series with random projection and phase alignment.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Vector quantization pretraining for EEG time series with random projection and phase alignment

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:20:13.483582Z

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Observation 0f3a9a3c-d9da-474e-af9e-1c8f0d9c059c · outbound

This paper cites Learning topology-agnostic EEG representations with geometry-aware modeling.Advances in Neural Information Processing Systems, 36, 2024.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Learning topology-agnostic EEG representations with geometry-aware modeling.Advances in Neural Information Processing Systems, 36, 2024

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:20:13.467692Z

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Observation 35e4cfd3-de09-4dd2-a815-32b3fbb4231e · outbound

This paper cites Brant: Foundation model for intracranial neural signal.Advances in Neural Information Processing Systems, 36, 2024.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Brant: Foundation model for intracranial neural signal.Advances in Neural Information Processing Systems, 36, 2024

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:20:13.451618Z

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Observation 1bf9a632-ddc1-4870-b009-a480ab24c616 · outbound

This paper cites Towards domain-free transformer for generalized EEG pre-training.IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2024.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Towards domain-free transformer for generalized EEG pre-training.IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2024

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:20:13.436108Z

Source-reported events for the cited work

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Observation 03e1c89e-b7b4-4147-ad07-cb554bc484f2 · outbound

This paper cites Reliable evaluation of functional connectivity and graph theory measures in source-level EEG: How many electrodes are enough?Clinical Neurophysiology, 150:1–16, 2023.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Reliable evaluation of functional connectivity and graph theory measures in source-level EEG: How many electrodes are enough?Clinical Neurophysiology, 150:1–16, 2023

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:20:13.420969Z

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Observation 4299b88a-0403-4bbd-84b3-350eebe9d559 · outbound

This paper cites Effects of depth electrode montage and single-pulse electrical stimulation sites on neuronal responses and effective connectivity.Clinical Neurophysiology, 131(12):2781–2792, 2020.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Effects of depth electrode montage and single-pulse electrical stimulation sites on neuronal responses and effective connectivity.Clinical Neurophysiology, 131(12):2781–2792, 2020

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:20:13.403473Z

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Observation 33b54284-130c-4251-8ab0-947a69cec308 · outbound

This paper cites Progress in brain computer interface: Challenges and opportunities.Frontiers in Systems Neuroscience, 15:578875, 2021.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Progress in brain computer interface: Challenges and opportunities.Frontiers in Systems Neuroscience, 15:578875, 2021

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:20:13.387375Z

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Observation eb47cb01-6793-48e6-93a6-db4560fe34eb · outbound

This paper cites Advances in human intracranial electroencephalography research, guidelines and good practices.Neuroimage, 260:119438, 2022.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Advances in human intracranial electroencephalography research, guidelines and good practices.Neuroimage, 260:119438, 2022

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:20:13.371549Z

Source-reported events for the cited work

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Observation 0c0f82ac-2990-4241-84b3-00e5b472ceb6 · outbound

This paper cites Heterogeneity of resting-state EEG features in juvenile myoclonic epilepsy and controls.Brain Communications, 4(4):fcac180, 2022.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Heterogeneity of resting-state EEG features in juvenile myoclonic epilepsy and controls.Brain Communications, 4(4):fcac180, 2022

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:20:13.354928Z

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Observation 19e9e508-7d40-4ef6-bf1d-689052acbda4 · outbound

This paper cites Developmental atlas of phase-amplitude coupling between physiologic high-frequency oscillations and slow waves.Nature Communications, 14(1):6435, 2023.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Developmental atlas of phase-amplitude coupling between physiologic high-frequency oscillations and slow waves.Nature Communications, 14(1):6435, 2023

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:20:13.338478Z

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Observation ec0612ed-5ffd-436b-8cdb-d5bf25e0b865 · outbound

This paper cites Masked autoencoders are scalable vision learners.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Masked autoencoders are scalable vision learners

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T19:20:12.630330Z

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Observation 3651da98-bdcb-48e6-86f8-5b848572b661 · outbound

This paper cites Neural fragility as an EEG marker of the seizure onset zone.Nature Neuroscience, 24(10):1465–1474, 2021.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Neural fragility as an EEG marker of the seizure onset zone.Nature Neuroscience, 24(10):1465–1474, 2021

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:20:13.312565Z

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Observation 4b76eac7-52b9-4bd4-a4bd-05a4e68c7c4f · outbound

This paper cites Resection of high frequency oscillations predicts seizure outcome in the individual patient.Scientific Reports, 7(1):13836, 2017.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Resection of high frequency oscillations predicts seizure outcome in the individual patient.Scientific Reports, 7(1):13836, 2017

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:20:13.297711Z

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Observation f7dbc9ec-2e08-4a68-88d5-27222fbcb2d6 · outbound

This paper cites Dataset of EEG recordings of pediatric patients with epilepsy based on the 10-20 system, 2021.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Dataset of EEG recordings of pediatric patients with epilepsy based on the 10-20 system, 2021

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:20:13.282854Z

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This paper cites A practical workflow for organizing clinical intraoperative and long-term iEEG data in BIDS.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer A practical workflow for organizing clinical intraoperative and long-term iEEG data in BIDS

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:20:13.267722Z

Source-reported events for the cited work

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Observation 668392bc-19e5-436e-b42b-8c448a9137ae · outbound

This paper cites Epilepsy iEEG interictal multicenterdataset, 2023.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Epilepsy iEEG interictal multicenterdataset, 2023

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:20:13.253052Z

Source-reported events for the cited work

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Observation 9f16c9bd-6be4-4b65-9b8d-6dfe69ad0df4 · outbound

This paper cites Normative intracranial EEG maps epileptogenic tissues in focal epilepsy.Brain, 145(6):1949–1961, 2022.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Normative intracranial EEG maps epileptogenic tissues in focal epilepsy.Brain, 145(6):1949–1961, 2022

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:20:13.238590Z

Source-reported events for the cited work

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Observation 3b4f187d-e978-4146-b902-9efe5b5e6eb5 · outbound

This paper cites Informa- tion flows from hippocampus to auditory cortex during replay of verbal working memory items.eLife, 11:e78677, 2022.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Informa- tion flows from hippocampus to auditory cortex during replay of verbal working memory items.eLife, 11:e78677, 2022

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:20:13.223968Z

Source-reported events for the cited work

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Observation 7d38ec04-26d1-4812-afcd-7d5e58212f4e · outbound

This paper cites Refining epileptogenic high-frequency oscillations using deep learning: A reverse engineering approach.Brain Communications, 4(1):fcab267, 2022.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Refining epileptogenic high-frequency oscillations using deep learning: A reverse engineering approach.Brain Communications, 4(1):fcab267, 2022

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:20:13.208679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f5b51893-64e7-44b2-a7a6-6b80eaa2ce6e · outbound

This paper cites Big data resources for EEGs: Enabling deep learning research.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Big data resources for EEGs: Enabling deep learning research

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:20:13.191708Z

Source-reported events for the cited work

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Observation 3bbb1b6f-9306-43a0-9195-90c1b0008861 · outbound

This paper cites The temple university hospital seizure detection corpus.Frontiers in Neuroinformatics, 12:83, 2018.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer The temple university hospital seizure detection corpus.Frontiers in Neuroinformatics, 12:83, 2018

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:20:13.176518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 075b9984-2139-4b74-a765-358779f5084e · outbound

This paper cites EEG synchronization analysis for seizure prediction: A study on data of noninvasive recordings.Processes, 8(7):846, 2020.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer EEG synchronization analysis for seizure prediction: A study on data of noninvasive recordings.Processes, 8(7):846, 2020

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:20:13.161469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation af975a8b-ff73-4b9a-bca3-21094cc8eaf0 · outbound

This paper cites Epileptic EEG dataset.Mendeley Data, 1, 2021.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Epileptic EEG dataset.Mendeley Data, 1, 2021

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:20:13.144662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9c58a91d-e11b-484c-a3f9-ddac095e235e · outbound

This paper cites Annotated interictal discharges in intracranial EEG sleep data and related machine learning detection scheme.Scientific Data, 11(1):1354, 2024.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Annotated interictal discharges in intracranial EEG sleep data and related machine learning detection scheme.Scientific Data, 11(1):1354, 2024

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:20:13.129491Z

Source-reported events for the cited work

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Observation 6e51e38e-8429-4478-a594-c7326daf1d6d · outbound

This paper cites PhD thesis, Massachusetts Institute of Technology, 2009.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer PhD thesis, Massachusetts Institute of Technology, 2009

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-15T19:20:12.700522Z

Source-reported events for the cited work

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Observation 8bca66e1-edb6-4d41-a8db-b8e5464b26a7 · outbound

This paper cites Scalp EEG recordings of pediatric epilepsy patients: A dataset for automatic detection of interictal epileptiform discharges from routine EEG.Data in Brief, 39:107680, 2021.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Scalp EEG recordings of pediatric epilepsy patients: A dataset for automatic detection of interictal epileptiform discharges from routine EEG.Data in Brief, 39:107680, 2021

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:20:13.103136Z

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Observation 9265d823-ccf5-443d-ad6f-bab43ce5bd42 · outbound

This paper cites A dataset of neonatal EEG recordings with seizure annotations.Scientific Data, 6(1):1–8, 2019.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer A dataset of neonatal EEG recordings with seizure annotations.Scientific Data, 6(1):1–8, 2019

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:20:13.086215Z

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Observation edf0b3ee-b8e6-4bc3-9a12-6d6958cc0ce3 · outbound

This paper cites Multicenter intracranial EEG dataset for classification of graphoelements and artifactual signals.Scientific Data, 7(1):179, 2020.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Multicenter intracranial EEG dataset for classification of graphoelements and artifactual signals.Scientific Data, 7(1):179, 2020

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:20:13.069826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f05993ae-d63e-4ff4-985f-d19f5949ae77 · outbound

This paper cites Tripod+ ai statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods.bmj, 385, 2024.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Tripod+ ai statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods.bmj, 385, 2024

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-15T19:20:12.717842Z

Source-reported events for the cited work

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Observation 6658b157-059d-4788-8f82-da0dda026082 · outbound

This paper cites Roformer: Enhanced transformer with rotary position embedding.Neurocomputing, 568:127063, 2024.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Roformer: Enhanced transformer with rotary position embedding.Neurocomputing, 568:127063, 2024

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Resolution: unresolved; no resolver link, observed 2026-08-15T19:20:12.722137Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 75eb19e2-1f9f-4dba-9890-53d4ed697a06 · outbound

This paper cites Similarity of neural network representations revisited.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Similarity of neural network representations revisited

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Resolution: unresolved; no resolver link, observed 2026-08-15T19:20:12.726346Z

Source-reported events for the cited work

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Observation 77aae4de-6fba-4303-9ac0-5df72683b09a · outbound

This paper cites MOMENT: A Family of Open Time-series Foundation Models.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer MOMENT: A Family of Open Time-series Foundation Models

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Resolution: unresolved; no resolver link, observed 2026-08-15T19:20:12.730665Z

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Observation c53f695e-186f-4504-bec0-fd72669668d3 · outbound

This paper cites A Time Series is Worth 64 Words: Long-term Forecasting with Transformers.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer A Time Series is Worth 64 Words: Long-term Forecasting with Transformers

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Resolution: unresolved; no resolver link, observed 2026-08-15T19:20:12.735725Z

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Observation 43a8b847-ed03-4ef0-bbca-9c6ab6d760ad · outbound

This paper cites Review of the bci competition iv.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Review of the bci competition iv

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Resolution: unresolved; no resolver link, observed 2026-08-15T19:20:12.740197Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 980476e7-07c8-4e38-93e4-84e99b3486c3 · outbound

This paper cites Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals.circulation, 101(23):e215–e220, 2000.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals.circulation, 101(23):e215–e220, 2000

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Resolution: unresolved; no resolver link, observed 2026-08-15T19:20:12.744898Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd026e72-8719-41c9-9949-1315fd7e448a · outbound

This paper cites EEG Conformer: Convolutional transformer for EEG decoding and visualization.IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31:710–719, 2022.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer EEG Conformer: Convolutional transformer for EEG decoding and visualization.IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31:710–719, 2022

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:20:13.004523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3fe4f17e-6c9b-4d37-bc78-22596d2bf963 · outbound

This paper cites CSP-Net: Common spatial pattern empowered neural networks for EEG-based motor imagery classification.Knowledge-Based Systems, 305:112668, 2024.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer CSP-Net: Common spatial pattern empowered neural networks for EEG-based motor imagery classification.Knowledge-Based Systems, 305:112668, 2024

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:20:12.987841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1d8a9939-f9af-4fa7-801b-d145fb3c88bf · outbound

This paper cites an unresolved cited work.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Unresolved cited work

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Resolution: unresolved; raw_fallback, observed 2026-08-15T19:20:12.969407Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 05dfc257-2fc5-4f1e-8adb-9ed53c859464 · outbound

This paper cites EEGNet: A compact convolutional neural network for EEG-based brain–computer interfaces.Journal of Neural Engineering, 15(5):056013, 2018.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer EEGNet: A compact convolutional neural network for EEG-based brain–computer interfaces.Journal of Neural Engineering, 15(5):056013, 2018

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:20:12.953417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 56638e8e-b1b9-4032-9735-c8bed95f4b57 · outbound

This paper cites TinySleepNet: An efficient deep learning model for sleep stage scoring based on raw single-channel EEG.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer TinySleepNet: An efficient deep learning model for sleep stage scoring based on raw single-channel EEG

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:20:12.932154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 48ba29e6-be88-4fbf-811d-011557197231 · outbound

This paper cites Physics-informed attention temporal convo- lutional network for EEG-based motor imagery classification.IEEE Transactions on Industrial Informatics, 19(2):2249–2258, 2022.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Physics-informed attention temporal convo- lutional network for EEG-based motor imagery classification.IEEE Transactions on Industrial Informatics, 19(2):2249–2258, 2022

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:20:12.915511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2433762c-4f92-4298-852a-139bb2a6ddae · outbound

This paper cites A DF-SSA analytical framework for revealing variations in multidimensional EEG features of epileptic seizures.Biomedical Signal Processing and Control, 100:107073, 2025.

Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer A DF-SSA analytical framework for revealing variations in multidimensional EEG features of epileptic seizures.Biomedical Signal Processing and Control, 100:107073, 2025

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:20:12.898164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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