{"url":"/task/eeg","name":"Electroencephalogram (EEG)","slug":"eeg","description_markdown":"**Electroencephalogram (EEG)** is a method of recording brain activity using electrophysiological indexes. When the brain is active, a large number of postsynaptic potentials generated synchronously by neurons are formed after summation. It records the changes of electric waves during brain activity and is the overall reflection of the electrophysiological activities of brain nerve cells on the surface of cerebral cortex or scalp. Brain waves originate from the postsynaptic potential of the apical dendrites of pyramidal cells. The formation of synchronous rhythm of EEG is also related to the activity of nonspecific projection system of cortex and thalamus. EEG is the basic theoretical research of brain science. EEG monitoring is widely used in its clinical application.","categories":[{"name":"Medical","url":"/area/medical"},{"name":"Methodology","url":"/area/methodology"},{"name":"Time Series","url":"/area/time-series"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"archive_url"},"counts":{"papers_tagged":1655,"papers_with_code":378,"benchmarks":3,"benchmark_tables_in_archive":3,"benchmark_tables_shown":3,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":7,"subtasks":7,"parent_tasks":0},"benchmarks":[{"leaderboard":"/sota/eeg-on-seed-iv","slug":"eeg-on-seed-iv","dataset":"SEED-IV","dataset_url":"/dataset/seed-1","rows_in_archive":3,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"BiHDM","paper_title":"A Novel Bi-hemispheric Discrepancy Model for EEG Emotion Recognition","paper_url":"/paper/a-novel-bi-hemispheric-discrepancy-model-for-1","paper_date":"2019-05-11","arxiv_id":"1906.01704","code_links":[],"syntology":null}},{"leaderboard":"/sota/eeg-on-hs-ssvep","slug":"eeg-on-hs-ssvep","dataset":"HS-SSVEP","dataset_url":null,"rows_in_archive":1,"metrics":["Accuracy (5-fold)"],"first_row_in_archive_order":{"model":"MultitaskSSVEP","paper_title":"Deep Multi-Task Learning for SSVEP Detection and Visual Response Mapping","paper_url":"/paper/deep-multi-task-learning-for-ssvep-detection","paper_date":"2020-10-10","arxiv_id":null,"code_links":[{"title":"jinglescode/ssvep-multi-task-learning","url":"https://github.com/jinglescode/ssvep-multi-task-learning"}],"syntology":null}},{"leaderboard":"/sota/eeg-on-seed","slug":"eeg-on-seed","dataset":"SEED","dataset_url":"/dataset/seed-1","rows_in_archive":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"DBN","paper_title":"Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks","paper_url":"/paper/investigating-critical-frequency-bands-and","paper_date":"2015-05-08","arxiv_id":null,"code_links":[],"syntology":null}}],"datasets":[{"url":"/dataset/seed-1","name":"SEED","full_name":"SJTU Emotion EEG Dataset","num_papers_in_archive":119},{"url":"/dataset/iclabel","name":"ICLabel","full_name":"","num_papers_in_archive":12},{"url":"/dataset/k-emocon","name":"K-EmoCon","full_name":"","num_papers_in_archive":9},{"url":"/dataset/mebal","name":"mEBAL","full_name":"","num_papers_in_archive":6},{"url":"/dataset/cwl-eeg-fmri-data-set","name":"CWL EEG/fMRI Dataset","full_name":"","num_papers_in_archive":3},{"url":"/dataset/mutla","name":"MUTLA","full_name":"","num_papers_in_archive":2},{"url":"/dataset/sparcnet","name":"SPaRCNet","full_name":"","num_papers_in_archive":0}],"subtasks":[{"url":"/task/attention-score-prediction","name":"Attention Score Prediction"},{"url":"/task/eeg-1","name":"EEG"},{"url":"/task/eeg-decoding","name":"Eeg Decoding"},{"url":"/task/eeg-denoising","name":"EEG Denoising"},{"url":"/task/lwr-classification","name":"LWR Classification"},{"url":"/task/noise-level-prediction","name":"Noise Level Prediction"},{"url":"/task/semanticity-prediction","name":"Semanticity prediction"}],"parent_tasks":[],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":30,"of":378,"tagged_in_all":1655,"items":[{"url":"/paper/sgdr-stochastic-gradient-descent-with-warm","title":"SGDR: Stochastic Gradient Descent with Warm Restarts","date":"2016-08-13","arxiv_id":"1608.03983","repositories_listed":24,"syntology":{"n":17,"n_ran":10,"n_unverified":7,"n_pointer_only":7}},{"url":"/paper/eegnet-a-compact-convolutional-network-for","title":"EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces","date":"2016-11-23","arxiv_id":"1611.08024","repositories_listed":11,"syntology":{"n":8,"n_ran":1,"n_unverified":7,"n_pointer_only":1}},{"url":"/paper/learning-representations-from-eeg-with-deep","title":"Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks","date":"2015-11-19","arxiv_id":"1511.06448","repositories_listed":11,"syntology":null},{"url":"/paper/deepsleepnet-a-model-for-automatic-sleep","title":"DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG","date":"2017-03-12","arxiv_id":"1703.04046","repositories_listed":8,"syntology":{"n":33,"n_ran":0,"n_unverified":33,"n_pointer_only":0}},{"url":"/paper/eegeyenet-a-simultaneous","title":"EEGEyeNet: a Simultaneous Electroencephalography and Eye-tracking Dataset and Benchmark for Eye Movement Prediction","date":"2021-11-06","arxiv_id":"2111.05100","repositories_listed":6,"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":0}},{"url":"/paper/u-time-a-fully-convolutional-network-for-time","title":"U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging","date":"2019-10-24","arxiv_id":"1910.11162","repositories_listed":5,"syntology":{"n":3,"n_ran":3,"n_unverified":0,"n_pointer_only":2}},{"url":"/paper/eeg-based-emotion-recognition-using","title":"EEG-Based Emotion Recognition Using Regularized Graph Neural Networks","date":"2019-07-18","arxiv_id":"1907.07835","repositories_listed":5,"syntology":null},{"url":"/paper/deep-learning-with-convolutional-neural","title":"Deep learning with convolutional neural networks for EEG decoding and visualization","date":"2017-03-15","arxiv_id":"1703.05051","repositories_listed":5,"syntology":null},{"url":"/paper/an-iot-endpoint-system-on-chip-for-secure-and","title":"An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics","date":"2016-12-18","arxiv_id":"1612.05974","repositories_listed":5,"syntology":null},{"url":"/paper/decoding-natural-images-from-eeg-for-object","title":"Decoding Natural Images from EEG for Object Recognition","date":"2023-08-25","arxiv_id":"2308.13234","repositories_listed":4,"syntology":{"n":12,"n_ran":7,"n_unverified":5,"n_pointer_only":0}},{"url":"/paper/priming-cross-session-motor-imagery","title":"Priming Cross-Session Motor Imagery Classification with A Universal Deep Domain Adaptation Framework","date":"2022-02-19","arxiv_id":"2202.09559","repositories_listed":3,"syntology":null},{"url":"/paper/transformer-based-spatial-temporal-feature","title":"Transformer-based Spatial-Temporal Feature Learning for EEG Decoding","date":"2021-06-11","arxiv_id":"2106.11170","repositories_listed":3,"syntology":{"n":1,"n_ran":0,"n_unverified":1,"n_pointer_only":1}},{"url":"/paper/advancing-nlp-with-cognitive-language","title":"Advancing NLP with Cognitive Language Processing Signals","date":"2019-04-04","arxiv_id":"1904.02682","repositories_listed":3,"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":1}},{"url":"/paper/seizurenet-a-deep-convolutional-neural","title":"SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type Classification","date":"2019-03-08","arxiv_id":"1903.03232","repositories_listed":3,"syntology":null},{"url":"/paper/sleepeegnet-automated-sleep-stage-scoring","title":"SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach","date":"2019-03-05","arxiv_id":"1903.02108","repositories_listed":3,"syntology":null},{"url":"/paper/deep-learning-based-electroencephalography","title":"Deep learning-based electroencephalography analysis: a systematic review","date":"2019-01-16","arxiv_id":"1901.05498","repositories_listed":3,"syntology":{"n":3,"n_ran":0,"n_unverified":3,"n_pointer_only":0}},{"url":"/paper/robustifying-independent-component-analysis","title":"Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise","date":"2018-06-04","arxiv_id":"1806.01094","repositories_listed":3,"syntology":{"n":1,"n_ran":0,"n_unverified":1,"n_pointer_only":1}},{"url":"/paper/comparative-evaluation-of-state-of-the-art","title":"Comparative evaluation of state-of-the-art algorithms for SSVEP-based BCIs","date":"2016-02-02","arxiv_id":"1602.00904","repositories_listed":3,"syntology":null},{"url":"/paper/artificial-intelligence-for-eeg-prediction","title":"Artificial Intelligence for EEG Prediction: Applied Chaos Theory","date":"2023-10-03","arxiv_id":"2402.03316","repositories_listed":2,"syntology":null},{"url":"/paper/vit2eeg-leveraging-hybrid-pretrained-vision","title":"ViT2EEG: Leveraging Hybrid Pretrained Vision Transformers for EEG Data","date":"2023-08-01","arxiv_id":"2308.00454","repositories_listed":2,"syntology":null},{"url":"/paper/sliced-wasserstein-on-symmetric-positive","title":"Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals","date":"2023-03-10","arxiv_id":"2303.05798","repositories_listed":2,"syntology":null},{"url":"/paper/closed-loop-bci-system-for-cybathlon-2020","title":"Closed loop BCI System for Cybathlon 2020","date":"2022-12-08","arxiv_id":"2212.04172","repositories_listed":2,"syntology":null},{"url":"/paper/towards-fast-single-trial-online-erp-based","title":"Towards Fast Single-Trial Online ERP based Brain-Computer Interface using dry EEG electrodes and neural networks: a pilot study","date":"2022-11-04","arxiv_id":"2211.10352","repositories_listed":2,"syntology":null},{"url":"/paper/a-transformer-based-deep-neural-network-model","title":"A Transformer-based deep neural network model for SSVEP classification","date":"2022-10-09","arxiv_id":"2210.04172","repositories_listed":2,"syntology":null},{"url":"/paper/clusterbma-bayesian-model-averaging-for","title":"clusterBMA: Bayesian model averaging for clustering","date":"2022-09-09","arxiv_id":"2209.04117","repositories_listed":2,"syntology":null},{"url":"/paper/physics-inform-attention-temporal","title":"Physics-inform attention temporal convolutional network for EEG-based motor imagery classification","date":"2022-08-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/changepoint-detection-in-noisy-data-using-a","title":"Changepoint Detection in Noisy Data Using a Novel Residuals Permutation-Based Method (RESPERM): Benchmarking and Application to Single Trial ERPs","date":"2022-04-21","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/beats-an-open-source-high-precision-multi","title":"BEATS: An Open-Source, High-Precision, Multi-Channel EEG Acquisition Tool System","date":"2022-03-04","arxiv_id":"2203.02102","repositories_listed":2,"syntology":null},{"url":"/paper/extracting-different-levels-of-speech","title":"Extracting Different Levels of Speech Information from EEG Using an LSTM-Based Model","date":"2021-06-17","arxiv_id":"2106.09622","repositories_listed":2,"syntology":null},{"url":"/paper/lggnet-learning-from-local-global-graph","title":"LGGNet: Learning from Local-Global-Graph Representations for Brain-Computer Interface","date":"2021-05-05","arxiv_id":"2105.02786","repositories_listed":2,"syntology":null}],"syntology_records":10,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}