{"url":"/dataset/seed-1","name":"SEED","full_name":"SJTU Emotion EEG Dataset","description_markdown":"The **SEED** dataset contains subjects' EEG signals when they were watching films clips. The film clips are carefully selected so as to induce different types of emotion, which are positive, negative, and neutral ones.\r\n\r\nSource: [http://bcmi.sjtu.edu.cn/home/seed/index.html](http://bcmi.sjtu.edu.cn/home/seed/index.html)\r\nImage Source: [http://bcmi.sjtu.edu.cn/home/seed/index.html](http://bcmi.sjtu.edu.cn/home/seed/index.html)","description_withheld":null,"homepage":"http://bcmi.sjtu.edu.cn/home/seed/index.html","introduced_date":"2015-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/investigating-critical-frequency-bands-and","title":"Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks","first_author":"Wei-Long Zheng","url":null},"license":{"name":"Custom (research-only, non-commercial)","url":"http://bcmi.sjtu.edu.cn/home/seed/resource/license/license.pdf"},"modalities":[{"name":"EEG","url":"/datasets/modality/eeg"}],"tasks":[{"name":"Emotion Recognition","url":"/task/emotion-recognition","datasets_with_task":"/datasets/task/emotion-recognition"},{"name":"Electroencephalogram (EEG)","url":"/task/eeg","datasets_with_task":"/datasets/task/eeg"},{"name":"EEG Emotion Recognition","url":"/task/eeg-emotion-recognition","datasets_with_task":"/datasets/task/eeg-emotion-recognition"}],"languages":[],"variants":["SEED-IV","　SEED","SEED"],"data_loaders":[{"repo":"https://github.com/probablygary/emotion-recognition","url":"https://github.com/probablygary/emotion-recognition","frameworks":[]}],"num_papers_in_archive":119,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/eeg-on-seed-iv","task":"Electroencephalogram (EEG)","dataset_variant":"SEED-IV","rows":3,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"BiHDM","paper":"/paper/a-novel-bi-hemispheric-discrepancy-model-for-1","metrics":{"Accuracy":"74.35"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/eeg-emotion-recognition-on-seed-iv","task":"EEG Emotion Recognition","dataset_variant":"SEED-IV","rows":2,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"RGNN","paper":"/paper/eeg-based-emotion-recognition-using","metrics":{"Accuracy":"79.37"},"code_links":[{"title":"zhongpeixiang/RGNN","url":"https://github.com/zhongpeixiang/RGNN"},{"title":"miracle-2001/gnn4eeg","url":"https://github.com/miracle-2001/gnn4eeg"},{"title":"mindspore-ai/contrib","url":"https://github.com/mindspore-ai/contrib/tree/master/intern/RGNN"},{"title":"pwc-1/Paper-9","url":"https://github.com/pwc-1/Paper-9/tree/main/7/RGNN"},{"title":"MindSpore-scientific/code-8","url":"https://github.com/MindSpore-scientific/code-8/tree/main/RGNN"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/eeg-on-seed","task":"Electroencephalogram (EEG)","dataset_variant":"SEED","rows":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"DBN","paper":"/paper/investigating-critical-frequency-bands-and","metrics":{"Accuracy":"86.08"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/emotion-recognition-on-seed","task":"Emotion Recognition","dataset_variant":"SEED","rows":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"4D-aNN","paper":"/paper/4d-attention-based-neural-network-for-eeg","metrics":{"Accuracy":"96.10"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/4d-attention-based-neural-network-for-eeg","title":"4D Attention-based Neural Network for EEG Emotion Recognition","date":"2021-01-14","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/eeg-based-emotion-recognition-using","title":"EEG-Based Emotion Recognition Using Regularized Graph Neural Networks","date":"2019-07-18","rows_on_this_dataset":2,"code_links":5,"syntology":null},{"paper":"/paper/a-novel-bi-hemispheric-discrepancy-model-for-1","title":"A Novel Bi-hemispheric Discrepancy Model for EEG Emotion Recognition","date":"2019-05-11","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/eeg-emotion-recognition-using-dynamical-graph","title":"EEG emotion recognition using dynamical graph convolutional neural networks","date":"2018-03-21","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/investigating-critical-frequency-bands-and","title":"Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks","date":"2015-05-08","rows_on_this_dataset":2,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}