Paper Citation Record · LEDGER

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models

As of 21 August 2026, this Paper Citation Record lists 86 of 86 outbound references and 8 inbound Pith citation observations for arXiv:2601.17883.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2601.17883 v3

Coverage vector

measured 86 of 86 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T06:20:08.920348Z

measured 94 of 94 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T15:09:46.017953Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

86 of 86 outbound references displayed

verified exact0
verified fuzzy0
unresolved85
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 1e61c910-6eb4-42b1-b145-ca8d41762958 · outbound

This paper cites Brain computer interfaces, a review,.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models Brain computer interfaces, a review,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T06:20:02.596753Z

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Observation a0beda98-6d7b-4234-82b8-ebabc2b4a527 · outbound

This paper cites Canine EEG helps human: Cross- species and cross-modality epileptic seizure detection via multi-space alignment,.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models Canine EEG helps human: Cross- species and cross-modality epileptic seizure detection via multi-space alignment,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T06:20:02.741733Z

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Observation ccf3f205-de66-45ce-ad7d-2476f7b7b758 · outbound

This paper cites Multimodal BCIs: Target detection, multidimensional control, and awareness evalu- ation in patients with disorder of consciousness,.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models Multimodal BCIs: Target detection, multidimensional control, and awareness evalu- ation in patients with disorder of consciousness,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T06:20:02.901716Z

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Observation d7d76e6b-5cc9-4f96-bdd1-cebf6c9cac97 · outbound

This paper cites Affective brain-computer interfaces (aBCIs): A tutorial,.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models Affective brain-computer interfaces (aBCIs): A tutorial,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T06:20:03.078284Z

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Observation eb2d23c8-403d-4053-893d-a91fefa801ac · outbound

This paper cites Artificial intelli- gence as an emerging technology in the current care of neurological disorders,.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models Artificial intelli- gence as an emerging technology in the current care of neurological disorders,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T06:20:03.195876Z

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Observation f91405f7-31b9-4088-8ab3-c4b91b3e7b95 · outbound

This paper cites Comparative analysis to identify efficient technique for interfacing BCI system,.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models Comparative analysis to identify efficient technique for interfacing BCI system,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T06:20:03.223206Z

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Observation 4a075853-dce1-4255-b249-88029bcdd252 · outbound

This paper cites A neuroergonomics approach to mental workload, engagement and human performance,.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models A neuroergonomics approach to mental workload, engagement and human performance,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T06:20:03.229454Z

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Observation ccf3bea7-42c3-42ca-94f4-98d24f9a593e · outbound

This paper cites Imagined speech can be decoded from low-and cross-frequency intracranial EEG features,.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models Imagined speech can be decoded from low-and cross-frequency intracranial EEG features,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T06:20:03.262122Z

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Observation bff01a34-5623-4846-a403-b1de41334b5c · outbound

This paper cites Magnetoen- cephalography (MEG) based non-invasive Chinese speech decoding,.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models Magnetoen- cephalography (MEG) based non-invasive Chinese speech decoding,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T06:20:03.317418Z

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Observation ddbea598-d608-43b5-b274-0abb1e52a269 · outbound

This paper cites EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces,.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T06:20:03.392398Z

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Observation 5ced12e7-6cb3-4686-8778-85503f064e5e · outbound

This paper cites EEG-based subject-independent emotion recognition using gated recurrent unit and minimum class confusion,.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models EEG-based subject-independent emotion recognition using gated recurrent unit and minimum class confusion,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T06:20:03.447350Z

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Observation 4ff3606b-69c0-4d8b-9535-a0da34b7a6de · outbound

This paper cites DBConformer: Dual-branch convolutional transformer for EEG decoding,.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models DBConformer: Dual-branch convolutional transformer for EEG decoding,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T06:20:03.492992Z

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Observation 9de7e451-78a2-49ed-8bc6-bef471ced57d · outbound

This paper cites SDDA: Spatial distillation based distribution alignment for cross-headset EEG classification,.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models SDDA: Spatial distillation based distribution alignment for cross-headset EEG classification,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T06:20:03.553460Z

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Observation 83bfb96b-b6d8-4004-82d7-a906d8ef917f · outbound

This paper cites AFPM: Alignment-based frame patch modeling for cross-dataset EEG decoding,.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models AFPM: Alignment-based frame patch modeling for cross-dataset EEG decoding,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T06:20:03.584035Z

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Observation 6ce9f19a-12ac-4864-ba56-cdaebb80e84c · outbound

This paper cites A comprehensive overview of large language models,.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models A comprehensive overview of large language models,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T06:20:03.649398Z

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Observation 163c7bc6-580a-49c3-a0a8-0fcc58698d89 · outbound

This paper cites Toward the unification of generative and discriminative visual foundation model: a survey,.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models Toward the unification of generative and discriminative visual foundation model: a survey,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T06:20:03.721709Z

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Observation eb5d516d-61b4-4566-9e97-453b35df22ac · outbound

This paper cites Foundation models for EEG decoding: current progress and prospective research,.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models Foundation models for EEG decoding: current progress and prospective research,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T06:20:03.778522Z

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Observation 37eac004-dd8b-4cb3-a21e-6d7aa3d1e400 · outbound

This paper cites BENDR: Using trans- formers and a contrastive self-supervised learning task to learn from massive amounts of EEG data,.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models BENDR: Using trans- formers and a contrastive self-supervised learning task to learn from massive amounts of EEG data,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T06:20:03.814296Z

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Observation 480b4b4b-12e3-4cd9-93e0-6c6d834342bc · outbound

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

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models BrainBERT: Self-supervised representation learning for intracranial recordings,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T06:20:03.837587Z

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Observation 09612e9b-2b27-44de-8e46-26aff02709c3 · outbound

This paper cites Mbrain: A multi-channel self-supervised learning framework for brain signals,.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models Mbrain: A multi-channel self-supervised learning framework for brain signals,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T06:20:03.861564Z

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Observation c2c2449d-9433-4ac0-b951-d312ba20b40f · outbound

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

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models BIOT: Biosignal transformer for cross-data learning in the wild,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T06:20:03.885612Z

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Observation df410049-92a9-4992-a56a-a635a416e628 · outbound

This paper cites Brant: Foundation model for intracranial neural signal,.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models Brant: Foundation model for intracranial neural signal,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T06:20:03.928622Z

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Observation 79c23a41-b35d-4247-a99b-52492ff99286 · outbound

This paper cites Large brain model for learning generic representations with tremendous EEG data in BCI,.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models Large brain model for learning generic representations with tremendous EEG data in BCI,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T06:20:03.958016Z

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Observation 69379f27-e8b3-44a2-b6ec-8d43e6136e7d · outbound

This paper cites Mentality: A Mamba-based Approach towards Foundation Models for EEG.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models Mentality: A Mamba-based Approach towards Foundation Models for EEG

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T06:20:03.979961Z

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Observation b74da3d8-68ea-44ee-9f5c-a78479689bc2 · outbound

This paper cites Neuro-GPT: Towards a foundation model for EEG,.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models Neuro-GPT: Towards a foundation model for EEG,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T06:20:04.001343Z

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Observation adf442ba-d305-4257-887d-81650b7f53c4 · outbound

This paper cites MEET: A multi-band EEG transformer for brain states decoding,.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models MEET: A multi-band EEG transformer for brain states decoding,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T06:20:04.012514Z

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Observation 6ead7f90-0179-4a07-8a37-cc141c599f8d · outbound

This paper cites EEGFormer: Towards transferable and interpretable large-scale EEG foundation model,.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models EEGFormer: Towards transferable and interpretable large-scale EEG foundation model,

Reference 27

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Observation fad2bffb-e983-4d19-b2ec-71dbc4095126 · outbound

This paper cites BrainWave: A Brain Signal Foundation Model for Clinical Applications.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models BrainWave: A Brain Signal Foundation Model for Clinical Applications

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T06:20:04.073412Z

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Observation a5f2b6a5-1564-4710-bc28-241b82f590ca · outbound

This paper cites NeuroLM: A universal multi- task foundation model for bridging the gap between language and EEG signals,.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models NeuroLM: A universal multi- task foundation model for bridging the gap between language and EEG signals,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-04T06:20:04.121054Z

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Observation 6b421aa6-dc28-43b8-8188-da694ab7319b · outbound

This paper cites Brant-X: A unified physiological signal alignment framework,.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models Brant-X: A unified physiological signal alignment framework,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-04T06:20:04.147990Z

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Observation 2b5a7398-b842-45af-ba9b-73d082831bed · outbound

This paper cites FoME: A Foundation Model for EEG using Adaptive Temporal-Lateral Attention Scaling.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models FoME: A Foundation Model for EEG using Adaptive Temporal-Lateral Attention Scaling

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-04T06:20:04.180219Z

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Observation a115226d-a127-47cd-8d4e-9dddda6f77e4 · outbound

This paper cites EEGPT: Pre- trained transformer for universal and reliable representation of EEG signals,.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models EEGPT: Pre- trained transformer for universal and reliable representation of EEG signals,

Reference 32

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Observation a42542ca-046c-460f-8c48-93af47f8d7ef · outbound

This paper cites BrainGPT: Unleashing the Potential of EEG Generalist Foundation Model by Autoregressive Pre-training.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models BrainGPT: Unleashing the Potential of EEG Generalist Foundation Model by Autoregressive Pre-training

Reference 33

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Observation 101fe524-88a2-4306-b88d-bfb7625a4e57 · outbound

This paper cites GEFM: Graph-enhanced EEG foundation model,.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models GEFM: Graph-enhanced EEG foundation model,

Reference 34

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Observation 04995b1b-c07d-4e13-b379-369731b8af38 · outbound

This paper cites CBramod: A criss-cross brain foundation model for EEG decoding,.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models CBramod: A criss-cross brain foundation model for EEG decoding,

Reference 35

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Observation 123f922c-5d8d-4f8e-9514-34cd5fd7d675 · outbound

This paper cites CERebro: Compact encoder for representations of brain oscillations using efficient alternating attention,.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models CERebro: Compact encoder for representations of brain oscillations using efficient alternating attention,

Reference 36

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Observation 38fa389a-aac0-496f-8ef1-2f6133fc2ce4 · outbound

This paper cites LEAD: Large foundation model for EEG-based alzheimer’s disease detection,.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models LEAD: Large foundation model for EEG-based alzheimer’s disease detection,

Reference 37

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This paper cites FEMBA: Efficient and scalable EEG analysis with a bidirectional mamba foun- dation model,.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models FEMBA: Efficient and scalable EEG analysis with a bidirectional mamba foun- dation model,

Reference 38

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Observation 6873ec6a-d1b1-470b-bfa9-afa647e0324d · outbound

This paper cites Large Cognition Model: Towards Pretrained EEG Foundation Model.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models Large Cognition Model: Towards Pretrained EEG Foundation Model

Reference 39

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Observation 6aabd3db-bc37-4536-b852-1e9bf7d2d8b7 · outbound

This paper cites Tokenizing single- channel EEG with time-frequency motif learning,.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models Tokenizing single- channel EEG with time-frequency motif learning,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-04T06:20:04.720162Z

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Observation 16149b8a-775f-413f-b9a5-64be105c9258 · outbound

This paper cites ALFEE: Adaptive Large Foundation Model for EEG Representation.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models ALFEE: Adaptive Large Foundation Model for EEG Representation

Reference 41

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Observation 50f39a5a-6e34-4f58-9c23-caedd60b8631 · outbound

This paper cites Brainomni: A brain foundation model 24 for unified EEG and MEG signals,.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models Brainomni: A brain foundation model 24 for unified EEG and MEG signals,

Reference 42

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Observation 2ffadb35-c7bc-47a9-a9f7-4786267d47b5 · outbound

This paper cites EEG Foundation Models for BCI Learn Diverse Features of Electrophysiology.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models EEG Foundation Models for BCI Learn Diverse Features of Electrophysiology

Reference 43

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Observation 0a9e26c4-d775-4796-96de-7f21d64470a9 · outbound

This paper cites CodeBrain: Bridging Decoupled Tokenizer and Multi-Scale Architecture for EEG Foundation Model.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models CodeBrain: Bridging Decoupled Tokenizer and Multi-Scale Architecture for EEG Foundation Model

Reference 44

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Observation ee1ad69b-8819-4be9-898a-f870f4f41f40 · outbound

This paper cites UniMind: Unleashing the Power of LLMs for Unified Multi-Task Brain Decoding.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models UniMind: Unleashing the Power of LLMs for Unified Multi-Task Brain Decoding

Reference 45

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Observation c94c6490-d679-4607-a4f3-3d52b430d4a8 · outbound

This paper cites CSBrain: A cross-scale spatiotemporal brain foundation model for EEG decoding,.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models CSBrain: A cross-scale spatiotemporal brain foundation model for EEG decoding,

Reference 46

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Observation 481dc417-0c57-4be9-af6a-e2684ef0d679 · outbound

This paper cites DMAE-EEG: A pretraining framework for EEG spatiotemporal representation learning,.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models DMAE-EEG: A pretraining framework for EEG spatiotemporal representation learning,

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-04T06:20:05.273147Z

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Observation c4ba48ff-9022-4293-a5a0-07a37d65c877 · outbound

This paper cites EEGMamba: An EEG foundation model with mamba,.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models EEGMamba: An EEG foundation model with mamba,

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-04T06:20:05.382890Z

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Observation 8ca3c3c0-2732-44b2-8017-39c13aa0c3b4 · outbound

This paper cites MIRepNet: A Pipeline and Foundation Model for EEG-Based Motor Imagery Classification.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models MIRepNet: A Pipeline and Foundation Model for EEG-Based Motor Imagery Classification

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-04T06:20:05.509596Z

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Observation e3b6dc0d-5401-44c0-8ee2-b5eca5adbbbf · outbound

This paper cites Foundation models reveal untapped health information in human polysomnographic sleep data,.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models Foundation models reveal untapped health information in human polysomnographic sleep data,

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-04T06:20:05.613177Z

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Observation 3599cba3-c6f3-4e8e-ad01-7ba96239c80b · outbound

This paper cites EEGDM: EEG Representation Learning via Generative Diffusion Model.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models EEGDM: EEG Representation Learning via Generative Diffusion Model

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-04T06:20:05.777113Z

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Observation 0f6cf8c4-8cc9-47c6-a6ad-8d4e569bdcb7 · outbound

This paper cites CoMET: A Contrastive-Masked Brain Foundation Model for Universal EEG Representation.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models CoMET: A Contrastive-Masked Brain Foundation Model for Universal EEG Representation

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-04T06:20:05.891617Z

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Observation 34817723-baec-4827-84dd-4f2c08773a6e · outbound

This paper cites EpilepsyFM: A domain-specific foundation model for epileptic representation learning using EEG signals,.

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models EpilepsyFM: A domain-specific foundation model for epileptic representation learning using EEG signals,

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Observation 28e07d4e-76ce-4d10-8a48-c73ae3f91779 · outbound

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EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models SingLEM: Single-Channel Large EEG Model

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Observation 82e66bdf-5543-44e8-9ae1-e9d3f8e60a6a · outbound

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EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models BrainPro: Towards Large-scale Brain State-aware EEG Representation Learning

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EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models Uni-NTFM: A unified foundation model for eeg signal representation learning,

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Observation a1dc683f-262c-43f1-944a-c7bf6d75876d · outbound

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EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models ELASTIQ: EEG-language align- ment with semantic task instruction and querying,

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EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models Neural codecs as biosignal tokenizers,

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EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models HEAR: An EEG foundation model with heterogeneous electrode adaptive representation,

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Observation c82306ae-28b3-4958-92ec-1ce51eff2313 · outbound

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EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models NeuroRVQ: Multi-Scale Biosignal Tokenization for Generative Foundation Models

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Observation 8280c8c4-f022-4e35-a760-f2d32025e53e · outbound

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EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models REVE: A foundation model for EEG-adapting to any setup with large-scale pretraining on 25,000 subjects,

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Observation 822e3c15-69ee-4af4-a696-3ff5dcdb3179 · outbound

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EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models Multi-dataset joint pre- training of emotional EEG enables generalizable affective computing,

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Observation f9188eac-2b77-4681-aea8-84d199655297 · outbound

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EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models LUNA: Efficient and topology-agnostic foundation model for EEG signal analysis,

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Observation d91337c2-3ad3-4fa7-990e-fb768a55c9f5 · outbound

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EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models THD-BAR: Topology hierarchical derived brain autoregressive modeling for EEG generic rep- resentations,

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Observation 6feb5d52-e0c0-4914-8054-d37441426e38 · outbound

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EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models EEG-X: Device-agnostic and noise-robust foundation model for EEG,

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Observation 16ff4d1f-a2cf-47e9-b97a-13f2c7e575f1 · outbound

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EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models SAMBA: Toward a long-context EEG foundation model via spatial embedding and differential mamba,

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Observation 20a6eda8-0c91-45f6-8984-ce65d7ad5334 · outbound

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EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models DeeperBrain: A neuro-grounded EEG foundation model towards universal BCI,

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Observation 85b77087-07b5-4f53-9a38-691dcd5b135c · outbound

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EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models Spatial and temporal resolutions of EEG: Is it really black and white? a scalp current density view,

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Observation 4580f008-fc3d-493b-a31a-6862d8b09e9f · outbound

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EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models Foundation models: A new paradigm for artificial intelligence,

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Observation 6b536e96-1d7c-481a-b72c-3b52c7880d66 · outbound

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EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models Foundation models defining a new era in vision: a survey and outlook,

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Observation d7763260-87b0-4f63-abb0-e89e47ddbbb6 · outbound

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EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models CLEAN-MI: A Scalable and Efficient Pipeline for Constructing High-Quality Neurodata in Motor Imagery Paradigm

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Observation 9a2f4394-c311-41ff-b697-1b66478e1460 · outbound

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EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models Transfer learning for brain-computer interfaces: A Euclidean space data alignment approach,

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Observation e40bab25-3ec8-46a2-bd83-bc9c27a37c80 · outbound

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EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models Revisiting Euclidean alignment for transfer learning in EEG-based brain-computer interfaces,

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EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models Optimizing spatial filters for robust EEG single-trial analysis,

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Observation 98643798-82ba-41a5-95d0-bfd7508fae8c · outbound

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EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models xDAWN algorithm to enhance evoked potentials: application to brain–computer interface,

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Observation 99deb68c-c23f-4699-a7aa-22156eb6141a · outbound

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EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models Classification of EEG evoked in 2d and 3d virtual reality: traditional machine learning versus deep learning,

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Observation 13b44c5d-d49b-4823-88b8-c7fd119a4c12 · outbound

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EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models Temporal feature extraction and machine learning for classification of sleep stages using telemetry polysomnography,

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EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models Enhancing detection of SSVEPs for a high-speed brain speller using task-related component analysis,

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Observation 410757bb-da1e-4c5e-9307-429ac4f90e66 · outbound

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EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models Multimodal vigilance estimation using deep learning,

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Observation 728ee6ad-b768-4ee0-9df0-88f668dd29dc · outbound

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EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models Deep learning with convolutional neural networks for EEG decoding and visualization,

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Observation 3c7c80f6-28fa-40da-b2e8-491ceb6adddf · outbound

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EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models LMDA-Net: A lightweight multi-dimensional attention network for general EEG-based brain- computer interfaces and interpretability,

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Observation 0f6aca91-d1aa-47f3-b7d3-d85f668b68e9 · outbound

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EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models Transformer convolutional neural networks for automated artifact detection in scalp EEG,

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Observation cb509e3c-c78a-4e93-8630-68b8ab60c9d9 · outbound

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EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models EEG-Deformer: A dense convolutional transformer for brain-computer interfaces,

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EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models EEG conformer: Convolutional transformer for EEG decoding and visualization,

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Observation 2f8ad6b3-cab9-476e-bc1f-a92009df75c3 · outbound

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EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models A survey of wearable lower extremity neurorehabilitation exoskeleton: Sensing, gait dynamics, and human–robot collaboration,

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Observation 588fc945-4862-411f-a52b-53e711ef0a91 · outbound

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Observation 572e6108-397c-4c7b-93fd-e1442ea61059 · inbound

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Observation 6320ea97-662b-433b-b737-13013a68c5d2 · inbound

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Observation 4ba9e428-08f8-4012-a23b-e930e5cf5c4e · inbound

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Observation 1427b211-61ca-426f-b9d9-2bf6c75a9baf · inbound

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