Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models | Paper Citation Record

Observation a9ff68f1-a64e-45cf-bd3d-f437923a9dd9 · outbound

This paper cites Event- related potential studies of emotion regulation: A review of recent progress and future directions.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Event- related potential studies of emotion regulation: A review of recent progress and future directions

Reference 1

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No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 6dbec345-bd53-4307-a87d-0a41aba33b6b · outbound

This paper cites Evoked and event-related potentials as biomarkers of consciousness state and recovery.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Evoked and event-related potentials as biomarkers of consciousness state and recovery

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.598501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 61f88477-2a7c-43ee-830d-e31bf8eec177 · outbound

This paper cites Recording and interpreting event-related potentials.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Recording and interpreting event-related potentials

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.596295Z

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No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 5f5a40a0-b43d-4555-b69d-5cd39745bbc2 · outbound

This paper cites Elec- troencephalography (eeg) and event-related potentials (erps) with human participants.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Elec- troencephalography (eeg) and event-related potentials (erps) with human participants

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.532155Z

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No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation d902d7c7-cc86-4ee8-ba41-e11b4631c07d · outbound

This paper cites Event-related potentials.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Event-related potentials

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.560984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 82683d47-aefb-4ac0-a66d-86ade9851ed5 · outbound

This paper cites Cognitive neurophysiology: Event-related potentials.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Cognitive neurophysiology: Event-related potentials

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.504574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 257bd378-c6bf-4565-9fdb-b6c5488d3065 · outbound

This paper cites Methods for acquiring and analyzing infant event-related potentials.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Methods for acquiring and analyzing infant event-related potentials

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.492521Z

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No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation e30db2de-1816-428c-a116-97108d6d540c · outbound

This paper cites A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.489921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation ea8d8ebe-e75a-4588-8745-c0428d313b9f · outbound

This paper cites Event-related potential: An overview.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Event-related potential: An overview

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.494711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 4e8378a8-613e-4697-bad3-e02a50b37545 · outbound

This paper cites Motor imagery eeg signal classiﬁcation using novel deep learning algorithm.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Motor imagery eeg signal classiﬁcation using novel deep learning algorithm

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.485084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 049cadc7-f912-4737-a5de-7db45075e16d · outbound

This paper cites Neuript: Foundation model for neural interfaces.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Neuript: Foundation model for neural interfaces

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.487641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 4b5913bc-c8e5-4bd3-bd3c-be4c06261803 · outbound

This paper cites Fapex: Fractional amplitude-phase expressor for robust cross-subject seizure prediction.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Fapex: Fractional amplitude-phase expressor for robust cross-subject seizure prediction

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.482834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 770d4fa2-12b2-4f9e-aea3-8f2460aff72d · outbound

This paper cites ADformer: A Multi-Granularity Spatial-Temporal Transformer for EEG-Based Alzheimer Detection.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models ADformer: A Multi-Granularity Spatial-Temporal Transformer for EEG-Based Alzheimer Detection

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-16T18:38:15.914683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 22f8f39b-7728-474d-bd6c-8bd2eed46177 · outbound

This paper cites GPT-4 Technical Report.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models GPT-4 Technical Report

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-05-16T18:38:15.910278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation d63fee02-0548-4c3f-9f95-281a7ac782b9 · outbound

This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-05-16T18:38:15.931200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 3c1eaa9f-5dc6-4281-a640-a8d41b4fdfc7 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models DINOv2: Learning Robust Visual Features without Supervision

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-05-16T18:38:15.888194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 5ce1ef08-7e13-4e31-b27f-867ae8ae102e · outbound

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

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI

Reference 17

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T18:38:15.926688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation f3815393-9dd4-49b6-ade3-263d5704d489 · outbound

This paper cites Eegpt: Pretrained transformer for universal and reliable rep- resentation of eeg signals.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Eegpt: Pretrained transformer for universal and reliable rep- resentation of eeg signals

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.502087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 0a43d8e8-dab5-49c4-adcf-05bbd531b9f9 · outbound

This paper cites Cbramod: A criss-cross brain foundation model for eeg decoding.arXiv preprint arXiv:2412.07236, 2024a.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Cbramod: A criss-cross brain foundation model for eeg decoding.arXiv preprint arXiv:2412.07236, 2024a

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-16T18:38:15.884644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 59ccf10c-5337-491f-af93-a9981734b24b · outbound

This paper cites Enhancing motor imagery eeg signal decoding through machine learning: A systematic review of recent progress.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Enhancing motor imagery eeg signal decoding through machine learning: A systematic review of recent progress

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.461886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 5eeb520d-8d9e-42c2-b83c-fe1c611aca8b · outbound

This paper cites Eeg window length evaluation for the detection of alzheimers disease over different brain regions.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Eeg window length evaluation for the detection of alzheimers disease over different brain regions

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.453229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 7f4eaf7b-cffd-42ea-89f5-a4f7056d618d · outbound

This paper cites Analysis of electroencephalographic signals complexity regarding alzheimer’s disease.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Analysis of electroencephalographic signals complexity regarding alzheimer’s disease

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.455384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation f635b8d4-75f0-40e2-be07-4c2706b6b35e · outbound

This paper cites Extracting salient features for eeg-based diagnosis of alzheimer’s disease using support vector machine classiﬁer.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Extracting salient features for eeg-based diagnosis of alzheimer’s disease using support vector machine classiﬁer

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.466290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation ea5182b1-b3e4-42a5-9829-2a953b32d542 · outbound

This paper cites Eeg correlates of p300-based brain–computer interface (bci) performance in people with amyotrophic lateral sclerosis.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Eeg correlates of p300-based brain–computer interface (bci) performance in people with amyotrophic lateral sclerosis

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.477911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 48536ceb-8803-43f5-811c-fd7e6825df72 · outbound

This paper cites Identifying patients with poststroke mild cognitive impairment by pattern recognition of working memory load-related erp.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Identifying patients with poststroke mild cognitive impairment by pattern recognition of working memory load-related erp

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.450916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 16874b81-1f8a-4c0e-b897-64986c68100c · outbound

This paper cites Eeg/erp: Within episodic assess- ment framework for cognition.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Eeg/erp: Within episodic assess- ment framework for cognition

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.446327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 40601930-ec6d-45a7-a3da-15404f68ce48 · outbound

This paper cites Characterizing alzheimers disease severity via resting-awake eeg amplitude modulation analysis.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Characterizing alzheimers disease severity via resting-awake eeg amplitude modulation analysis

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.448632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 2096ff86-6ee9-4945-b081-69322f9b1774 · outbound

This paper cites Network substrates of cognitive im- pairment in alzheimers disease.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Network substrates of cognitive im- pairment in alzheimers disease

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.468654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 779651aa-dc33-4f61-8982-0e575f3b4346 · outbound

This paper cites Quantifying synchrony patterns in the eeg of alzheimers patients with linear and non-linear connectivity markers.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Quantifying synchrony patterns in the eeg of alzheimers patients with linear and non-linear connectivity markers

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.575290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 3488c8b3-6fc9-40f8-b010-2494f93345b3 · outbound

This paper cites Improving alzheimer’s disease diagnosis with machine learning techniques.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Improving alzheimer’s disease diagnosis with machine learning techniques

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.566115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 7b2d83ce-c7d1-49d4-b6f2-6ac1dcbdbd64 · outbound

This paper cites Index of alpha/theta ratio of the electroencephalogram: a new marker for alzheimers disease.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Index of alpha/theta ratio of the electroencephalogram: a new marker for alzheimers disease

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.563685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 65ba5e63-aa76-4234-bf48-3161a4e273a2 · outbound

This paper cites Multiple characteristics analysis of alzheimers electroencephalogram by power spectral density and lempel–ziv complexity.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Multiple characteristics analysis of alzheimers electroencephalogram by power spectral density and lempel–ziv complexity

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.570988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation b5ee5f2d-a40e-4dc2-a4fd-40fc79f6f317 · outbound

This paper cites Clinicians road map to wavelet eeg as an alzheimers disease biomarker.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Clinicians road map to wavelet eeg as an alzheimers disease biomarker

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.573158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 93099498-2f88-4b82-b0d5-bb8917acdd5c · outbound

This paper cites Quantitative eeg markers relate to alzheimers disease severity in the prospective dementia registry austria (prodem).

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Quantitative eeg markers relate to alzheimers disease severity in the prospective dementia registry austria (prodem)

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.577724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 5acf6deb-961a-433f-bc23-91d10597e5ce · outbound

This paper cites Multiscale ﬂuctuation-based dispersion entropy and its applications to neurological diseases.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Multiscale ﬂuctuation-based dispersion entropy and its applications to neurological diseases

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.580351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 1b88092b-18ec-452a-a203-8863c6432389 · outbound

This paper cites Unbiased estimation of permutation entropy in eeg analysis for alzheimer’s disease classiﬁcation.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Unbiased estimation of permutation entropy in eeg analysis for alzheimer’s disease classiﬁcation

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.586824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 80051eda-f483-444f-9ff7-0ac63c8a8256 · outbound

This paper cites Early screening of children with autism spectrum disorder based on elec- troencephalogram signal feature selection with l1-norm regularization.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Early screening of children with autism spectrum disorder based on elec- troencephalogram signal feature selection with l1-norm regularization

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.591752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 7a046288-8cbb-448c-8e14-fdd172880600 · outbound

This paper cites Eeg-based affective state recognition from human brain signals by using hjorth-activity.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Eeg-based affective state recognition from human brain signals by using hjorth-activity

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.470749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 23d723b5-924e-4c7e-a8a1-a521d09fa780 · outbound

This paper cites Diagnosis of alzheimers disease via machine learning approaches with integrated resting-state eeg and erp characteristics.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Diagnosis of alzheimers disease via machine learning approaches with integrated resting-state eeg and erp characteristics

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.473175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation fc127317-33d3-4bca-a320-fb513a066c36 · outbound

This paper cites Eegnet: a compact convolutional neural network for eeg-based brain–computer interfaces.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Eegnet: a compact convolutional neural network for eeg-based brain–computer interfaces

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.475616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 0a6ac271-513b-4ee8-b04c-3d3fc79b1e6c · outbound

This paper cites Eeg-inception: a novel deep convolutional neural network for assistive erp-based brain-computer interfaces.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Eeg-inception: a novel deep convolutional neural network for assistive erp-based brain-computer interfaces

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.457546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 8a94bbed-8609-4f05-a375-c7dc2301cbe6 · outbound

This paper cites An attention-based wavelet convolution neural network for epilepsy eeg classiﬁcation.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models An attention-based wavelet convolution neural network for epilepsy eeg classiﬁcation

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.459790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 43d795e3-12cc-4e0e-86cb-49102f218a4a · outbound

This paper cites A transformer-based approach combining deep learning network and spatial-temporal information for raw eeg classiﬁcation.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models A transformer-based approach combining deep learning network and spatial-temporal information for raw eeg classiﬁcation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.568730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation e2fe57e7-b467-45ad-8c0e-1fc5c72b8ed0 · outbound

This paper cites Eeg conformer model based epileptic seizure prediction using deep learning.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Eeg conformer model based epileptic seizure prediction using deep learning

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.464112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 25c710ea-1c62-47e2-a8be-f6e19f9bcd72 · outbound

This paper cites Neuro-BERT: Rethinking Masked Autoencoding for Self-supervised Neurological Pretraining.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Neuro-BERT: Rethinking Masked Autoencoding for Self-supervised Neurological Pretraining

Reference 45

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T18:38:15.892751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 66f05320-5729-4a71-b9a8-02d89470cab2 · outbound

This paper cites Lggnet: Learning from local-global-graph representations for brain–computer interface.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Lggnet: Learning from local-global-graph representations for brain–computer interface

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.480308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation e0bfcb6a-906d-440d-ac0d-604e3c5a2efe · outbound

This paper cites Mocnn: A multiscale deep convolutional neural network for erp-based brain-computer interfaces.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Mocnn: A multiscale deep convolutional neural network for erp-based brain-computer interfaces

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.444028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 1d2aa622-c644-47d0-826f-b241f1eb94d1 · outbound

This paper cites EEGMamba: Bidirectional State Space Model with Mixture of Experts for EEG Multi-task Classification.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models EEGMamba: Bidirectional State Space Model with Mixture of Experts for EEG Multi-task Classification

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-16T18:38:15.896657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 544c845b-9158-4529-a08a-482499c0da71 · outbound

This paper cites Repurposing Foundation Model for Generalizable Medical Time Series Classification.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Repurposing Foundation Model for Generalizable Medical Time Series Classification

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-05-16T18:38:15.901204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 010527e7-9cf0-44c8-96bd-9ee26dcdd582 · outbound

This paper cites NeuroLM: A Universal Multi-task Foundation Model for Bridging the Gap between Language and EEG Signals.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models NeuroLM: A Universal Multi-task Foundation Model for Bridging the Gap between Language and EEG Signals

Reference 50

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T18:38:15.905866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 4fe0d803-ec8d-4d37-a1fc-3bbe2cdd9535 · outbound

This paper cites Luna: Efﬁcient and topology-agnostic foundation model for eeg signal analy- sis.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Luna: Efﬁcient and topology-agnostic foundation model for eeg signal analy- sis

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.594111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 516a78b9-e1d9-4efc-b547-cc9778bdada8 · outbound

This paper cites Csbrain: A cross-scale spatiotemporal brain foundation model for eeg decoding.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Csbrain: A cross-scale spatiotemporal brain foundation model for eeg decoding

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.548338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 7422f66f-4b27-4917-97ca-3da15a034fb1 · outbound

This paper cites Economical assessment of working memory and response inhibition in adhd using a combined n-back/nogo paradigm: An erp study.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Economical assessment of working memory and response inhibition in adhd using a combined n-back/nogo paradigm: An erp study

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.540937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 4e5839e4-9f64-4182-9212-93627d551f23 · outbound

This paper cites Eeg: 3-stim auditory oddball and rest in parkinsons.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Eeg: 3-stim auditory oddball and rest in parkinsons

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.582413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 9b67db66-817b-4265-a448-78673be9fbb1 · outbound

This paper cites Cognitive electrophysiology in socioeconomic context in adulthood.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Cognitive electrophysiology in socioeconomic context in adulthood

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.538565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 848cc794-44d1-4802-8600-0ed537fe9f39 · outbound

This paper cites Erps predict symptomatic distress and recovery in sub-acute mild traumatic brain injury.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Erps predict symptomatic distress and recovery in sub-acute mild traumatic brain injury

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.543432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 53b5d297-32f0-42b1-bfa0-049b28c134ea · outbound

This paper cites The nencki-symfonia electroencephalography/event-related potential dataset: Multiple cognitive tasks and resting-state data collected in a sample of healthy adults.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models The nencki-symfonia electroencephalography/event-related potential dataset: Multiple cognitive tasks and resting-state data collected in a sample of healthy adults

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.545943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 8f8b94fc-ca0c-48ea-bcbf-9adf40da603b · outbound

This paper cites Evoked mid-frontal activity predicts cognitive dysfunction in parkinsons disease.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Evoked mid-frontal activity predicts cognitive dysfunction in parkinsons disease

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.556109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 75522b5c-fd66-48ff-99b7-092d46e0e3c8 · outbound

This paper cites An eeg marker of reward processing is diminished in parkinsons disease.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models An eeg marker of reward processing is diminished in parkinsons disease

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.529801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation ae2ac53b-32e7-41e7-9835-7b98db42b3a8 · outbound

This paper cites Mid-frontal theta activity is diminished during cognitive control in parkinson’s disease.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Mid-frontal theta activity is diminished during cognitive control in parkinson’s disease

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.507213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 7d5b524f-d632-49d6-84d5-c7e701bf003a · outbound

This paper cites Iclabel: An automated electroencephalographic independent component classi- ﬁer, dataset, and website.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Iclabel: An automated electroencephalographic independent component classi- ﬁer, dataset, and website

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.525954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation d604fb41-75df-4f9b-9b5a-47c5f51dde25 · outbound

This paper cites Enhanced gamma activity and cross-frequency interaction of resting-state electroencephalographic oscillations in patients with alzheimers disease.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Enhanced gamma activity and cross-frequency interaction of resting-state electroencephalographic oscillations in patients with alzheimers disease

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.499728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 9294671f-d784-46fe-a94a-bb184537a86a · outbound

This paper cites The effects of automated artifact removal algo- rithms on electroencephalography-based alzheimer’s disease diagnosis.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models The effects of automated artifact removal algo- rithms on electroencephalography-based alzheimer’s disease diagnosis

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.497350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 03407362-2817-4fe3-9e78-8484c5416dec · outbound

This paper cites Multiple feature extraction and classiﬁcation of electroencephalo- graph signal for alzheimers’ with spectrum and bispectrum.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Multiple feature extraction and classiﬁcation of electroencephalo- graph signal for alzheimers’ with spectrum and bispectrum

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.523889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 6cb0cfd0-f24d-4a47-bbe3-c4d5b129a9d5 · outbound

This paper cites Eeg in the diagnostics of alzheimers disease.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Eeg in the diagnostics of alzheimers disease

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.527761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 38c21366-6954-493e-a7bb-04b733434181 · outbound

This paper cites Predictive models in diagnosis of alzheimers disease from eeg.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Predictive models in diagnosis of alzheimers disease from eeg

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.536251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 9b5caeb3-f6f8-44f1-91ec-abb09d6f5430 · outbound

This paper cites Scale-free behaviour and metastable brain-state switching driven by human cognition, an empirical approach.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Scale-free behaviour and metastable brain-state switching driven by human cognition, an empirical approach

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.519779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 8910bea5-68fa-4622-895e-d407946086e3 · outbound

This paper cites Eeg alpha activity and the erp to target stimuli in an auditory oddball paradigm.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Eeg alpha activity and the erp to target stimuli in an auditory oddball paradigm

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.534325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 7bc1fb04-0795-4dd6-a94d-704f62ede4ca · outbound

This paper cites Temporal convolutional networks for action segmentation and detection.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Temporal convolutional networks for action segmentation and detection

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.550855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation bdff2b63-345e-4246-ab17-7ee055e9b4cc · outbound

This paper cites Moderntcn: A modern pure convolution structure for general time series analysis.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Moderntcn: A modern pure convolution structure for general time series analysis

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.553647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 753627a3-0474-4bae-a2ab-86be6482567f · outbound

This paper cites TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis

Reference 71

Resolution: verified exact; arxiv_id, observed 2026-05-16T19:09:17.286568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 3082b819-75bb-4582-8302-aa657d511dd2 · outbound

This paper cites A time series is worth 64words: Long-term forecasting with transformers.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models A time series is worth 64words: Long-term forecasting with transformers

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.589024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation f729dad9-e634-43db-8a13-9442f9ef30a7 · outbound

This paper cites itransformer: Inverted transformers are ef- fective for time series forecasting.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models itransformer: Inverted transformers are ef- fective for time series forecasting

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.517161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 3816836c-d73e-4082-b329-c83a65ed1bbd · outbound

This paper cites Med- former: A multi-granularity patching transformer for medical time-series classiﬁcation.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Med- former: A multi-granularity patching transformer for medical time-series classiﬁcation

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.512451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 41071017-084f-415a-beb2-2876795152f9 · outbound

This paper cites Towards multi-resolution spatiotempo- ral graph learning for medical time series classiﬁcation.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Towards multi-resolution spatiotempo- ral graph learning for medical time series classiﬁcation

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.584562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation ddb3d965-0c6f-43ee-86ef-30d6ffb5cb2d · outbound

This paper cites Xception: Deep learning with depthwise separable convolutions.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Xception: Deep learning with depthwise separable convolutions

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.509875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation cd1cd2dc-7b90-4376-aaa7-c85f264e2ef0 · outbound

This paper cites Eeg conformer: Convolutional transformer for eeg decoding and visu- alization.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Eeg conformer: Convolutional transformer for eeg decoding and visu- alization

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.514650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation b1a5aabf-b231-4318-9536-5b57496a370b · outbound

This paper cites Biot: Biosignal transformer for cross-data learning in the wild.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Biot: Biosignal transformer for cross-data learning in the wild

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.521892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation d4e8a086-5145-4c4d-bf14-6604a8bfe46a · outbound

This paper cites Lead: Large foundation model for eeg-based alzheimer’s disease detection.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models Lead: Large foundation model for eeg-based alzheimer’s disease detection

Reference 79

Resolution: verified exact; arxiv_id, observed 2026-05-16T18:38:15.922598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation f77aced7-e89d-4d70-b706-7f2eccc027f1 · outbound

This paper cites The temple university hospital eeg data corpus.

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models The temple university hospital eeg data corpus

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:38:16.558527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers