EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding | Paper Citation Record

Observation 4dd76e57-d463-49bd-90ce-e46f7cab3c6c · outbound

This paper cites Non-invasive brain-computer interfaces: state of the art and trends,.

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding Non-invasive brain-computer interfaces: state of the art and trends,

Reference 1

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This paper cites Eegpt: Pretrained transformer for universal and reliable representation of eeg signals,.

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding Eegpt: Pretrained transformer for universal and reliable representation of eeg signals,

Reference 2

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Observation 02c5d4cf-ae67-42e2-aa90-57a15a01efc7 · outbound

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

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI

Reference 3

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Observation 543aef01-8246-44c6-8ee4-964afebf10f1 · outbound

This paper cites Cbramod: A criss-cross brain foundation model for eeg decoding,.

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding Cbramod: A criss-cross brain foundation model for eeg decoding,

Reference 4

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Observation e16880cd-1b5b-499d-9eb7-c8cacf1788a5 · outbound

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

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding NeuroLM: A Universal Multi-task Foundation Model for Bridging the Gap between Language and EEG Signals

Reference 5

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Observation 0d6a438e-8368-406f-a1f1-4bb4538c8974 · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding,.

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding Bert: Pre-training of deep bidirectional transformers for language understanding,

Reference 6

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Observation 47df70e3-4822-430a-9faf-962f51605cb3 · outbound

This paper cites Language models are unsupervised multitask learners,.

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding Language models are unsupervised multitask learners,

Reference 7

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Observation dd064a65-ed1e-4b9b-9748-3bda0f7cde15 · outbound

This paper cites Eeg2rep: enhancing self-supervised eeg representation through informative masked inputs,.

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding Eeg2rep: enhancing self-supervised eeg representation through informative masked inputs,

Reference 8

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Observation d33796eb-61a3-46fc-ba80-b5ae17da2caf · outbound

This paper cites Wavelet2vec: A filter bank masked autoencoder for eeg-based seizure subtype classification,.

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding Wavelet2vec: A filter bank masked autoencoder for eeg-based seizure subtype classification,

Reference 9

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Observation 595da82b-ef67-4fdc-8224-ddf6b4ddbf6f · outbound

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

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding Biot: Biosignal transformer for cross-data learning in the wild,

Reference 10

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Observation a104070e-94a2-4824-964d-c9a619288a60 · outbound

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

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding MIRepNet: A Pipeline and Foundation Model for EEG-Based Motor Imagery Classification

Reference 11

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Observation 110a45fd-ee92-4d22-bd9c-0c438f82b194 · outbound

This paper cites Towards robust multimodal physiological foundation models: Handling arbitrary missing modali- ties,.

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding Towards robust multimodal physiological foundation models: Handling arbitrary missing modali- ties,

Reference 12

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Observation 8c8abbe4-1c76-4ac3-84d1-51ebbd1a8435 · outbound

This paper cites Sentence-bert: Sentence embeddings using siamese bert-networks,.

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding Sentence-bert: Sentence embeddings using siamese bert-networks,

Reference 13

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Observation e4314d2d-1058-468b-9738-34d66236e82a · outbound

This paper cites Mindfulness improves brain–computer interface performance by increasing control over neural activity in the alpha band,.

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding Mindfulness improves brain–computer interface performance by increasing control over neural activity in the alpha band,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:27:29.749148Z

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Observation 648b539d-0d93-4608-b6c7-1d075535dcc4 · outbound

This paper cites Identifying similarities and differences in emotion recognition with eeg and eye movements among chinese, german, and french people,.

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding Identifying similarities and differences in emotion recognition with eeg and eye movements among chinese, german, and french people,

Reference 15

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Observation 1c087bbf-3479-40d3-9848-8f5c97fa750a · outbound

This paper cites Investigating the effects of sleep conditions on emotion responses with eeg signals and eye movements,.

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding Investigating the effects of sleep conditions on emotion responses with eeg signals and eye movements,

Reference 16

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Observation eedc22de-2baa-4c4e-9db1-eafe4aaa475c · outbound

This paper cites Chineseeeg: A chinese linguistic corpora eeg dataset for semantic alignment and neural decoding,.

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding Chineseeeg: A chinese linguistic corpora eeg dataset for semantic alignment and neural decoding,

Reference 17

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Observation 19587b8c-a9ae-443d-a475-06469edf3908 · outbound

This paper cites Chisco: An eeg-based bci dataset for decoding of imagined speech,.

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding Chisco: An eeg-based bci dataset for decoding of imagined speech,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:27:29.705108Z

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Observation dff5039f-adcf-4d5f-9728-cd8896f1d24e · outbound

This paper cites Identification of perceived sentences using deep neural networks in eeg,.

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding Identification of perceived sentences using deep neural networks in eeg,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:27:29.694252Z

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Observation 779c362a-880f-4d5b-8eda-d36162bd7abe · outbound

This paper cites Thinking out loud, an open-access eeg-based bci dataset for inner speech recognition,.

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding Thinking out loud, an open-access eeg-based bci dataset for inner speech recognition,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:27:29.682582Z

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Observation a5ab3eaa-1aef-4cdd-bbaf-6d12b45b199c · outbound

This paper cites Eeg dataset and openbmi toolbox for three bci paradigms: An investigation into bci illiteracy,.

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding Eeg dataset and openbmi toolbox for three bci paradigms: An investigation into bci illiteracy,

Reference 21

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Observation 38f35d35-d89c-4f96-b206-658bc955381f · outbound

This paper cites Review of the bci competition iv,.

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding Review of the bci competition iv,

Reference 22

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Observation fa82e97f-74c3-4490-a1fe-3c0b59450312 · outbound

This paper cites 2020 international brain–computer interface competition: A review,.

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding 2020 international brain–computer interface competition: A review,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:27:29.655465Z

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Observation d81c9d52-02d9-4fff-b19f-a83958004305 · outbound

This paper cites A large eeg dataset for studying cross-session variability in motor imagery brain-computer interface,.

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding A large eeg dataset for studying cross-session variability in motor imagery brain-computer interface,

Reference 24

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This paper cites Deep learning with convolutional neural networks for eeg decoding and visualization,.

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding Deep learning with convolutional neural networks for eeg decoding and visualization,

Reference 25

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Observation 8f58ba3d-ce8d-421f-a228-104ae2ab830e · outbound

This paper cites Eeg datasets for motor imagery brain–computer interface,.

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding Eeg datasets for motor imagery brain–computer interface,

Reference 26

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Observation 98a09c2e-c610-4fe2-bfb6-fa6cea515f49 · outbound

This paper cites Open access dataset for eeg+ nirs single-trial classification,.

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding Open access dataset for eeg+ nirs single-trial classification,

Reference 27

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Observation 5f9042e2-9889-479c-aebe-7b0f263e0163 · outbound

This paper cites Bci2000: a general-purpose brain-computer interface (bci) system,.

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding Bci2000: a general-purpose brain-computer interface (bci) system,

Reference 28

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This paper cites Experimenters’ influ- ence on mental-imagery based brain-computer interface user training,.

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding Experimenters’ influ- ence on mental-imagery based brain-computer interface user training,

Reference 29

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Observation 0442ff66-f7c4-4a4a-a489-fede815458ba · outbound

This paper cites Evaluation of eeg oscillatory patterns and cognitive process during simple and compound limb motor imagery,.

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding Evaluation of eeg oscillatory patterns and cognitive process during simple and compound limb motor imagery,

Reference 30

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Observation aebef6d3-4619-4d16-bee6-44913b967c81 · outbound

This paper cites A large finer-grained affective computing eeg dataset,.

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding A large finer-grained affective computing eeg dataset,

Reference 31

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This paper cites Differential entropy feature for eeg-based emotion classification,.

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding Differential entropy feature for eeg-based emotion classification,

Reference 32

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Observation 030d1eac-5132-4969-ab02-8ab51ab3bd3a · outbound

This paper cites Comparing recognition performance and robustness of multimodal deep learning models for multimodal emotion recognition,.

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding Comparing recognition performance and robustness of multimodal deep learning models for multimodal emotion recognition,

Reference 33

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Observation 561abf2d-5246-4edd-9db7-fe71806674b5 · outbound

This paper cites Seed-vii: A multi- modal dataset of six basic emotions with continuous labels for emotion recognition,.

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding Seed-vii: A multi- modal dataset of six basic emotions with continuous labels for emotion recognition,

Reference 34

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Observation e678e947-30e4-4abb-a33c-e960b364e900 · outbound

This paper cites EEG data for ADHD / Control children,.

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding EEG data for ADHD / Control children,

Reference 35

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Observation 63b16a3d-1a1e-45b9-984d-e11b50497f6c · outbound

This paper cites Electroencephalograms during mental arithmetic task performance,.

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding Electroencephalograms during mental arithmetic task performance,

Reference 36

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Observation 86c9b048-746a-4e9e-a387-399c5f16a43c · outbound

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

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding Eegnet: a compact convolutional neural network for eeg-based brain–computer interfaces,

Reference 37

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Observation 8f78eabd-1b6c-4482-9299-09c583a25096 · outbound

This paper cites Tsception: Capturing temporal dynamics and spatial asymmetry from eeg for emo- tion recognition,.

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding Tsception: Capturing temporal dynamics and spatial asymmetry from eeg for emo- tion recognition,

Reference 38

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This paper cites Transformer-based Spatial-Temporal Feature Learning for EEG Decoding.

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding Transformer-based Spatial-Temporal Feature Learning for EEG Decoding

Reference 39

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This paper cites Eeg conformer: Convolutional transformer for eeg decoding and visualization,.

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding Eeg conformer: Convolutional transformer for eeg decoding and visualization,

Reference 40

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This paper cites A large eeg database with users’ profile information for motor imagery brain- computer interface research,.

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding A large eeg database with users’ profile information for motor imagery brain- computer interface research,

Reference 41

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Observation 6b9ac0cd-95ae-4ca9-a918-44657029980d · outbound

This paper cites Svm-enhanced attention mechanisms for motor imagery eeg classification in brain-computer interfaces,.

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding Svm-enhanced attention mechanisms for motor imagery eeg classification in brain-computer interfaces,

Reference 42

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Observation cbe73137-e967-49c6-825d-8da7e90f2c4f · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 43

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Observation 6bc99d74-231d-49e6-8ac8-fbfd05990891 · outbound

This paper cites DeBERTa: Decoding-enhanced BERT with Disentangled Attention.

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding DeBERTa: Decoding-enhanced BERT with Disentangled Attention

Reference 44

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Observation d2d4f6bf-35ed-451c-be84-b609185b64d2 · outbound

This paper cites Learning eeg representations with weighted convolutional siamese network: A large multi-session post- stroke rehabilitation study,.

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding Learning eeg representations with weighted convolutional siamese network: A large multi-session post- stroke rehabilitation study,

Reference 45

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Observation 4ab0a24d-fdbb-441d-9eaa-0a2edd8da1ef · outbound

This paper cites Emt: A novel transformer for generalized cross-subject eeg emotion recognition,.

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding Emt: A novel transformer for generalized cross-subject eeg emotion recognition,

Reference 46

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Observation e1698795-cfa9-4d50-9f95-dd8e95853ba9 · outbound

This paper cites Decoding covert speech from eeg by functional areas spatio-temporal transformer,.

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding Decoding covert speech from eeg by functional areas spatio-temporal transformer,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:27:29.498387Z

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