{"url":"/dataset/bci-competition-iv-ecog-to-hand-moves","name":"BCI Competition IV: ECoG to Finger Movements","full_name":null,"description_markdown":"#####Prediction of Finger Flexion IV Brain-Computer Interface Data Competition\r\n\r\nThe goal of this dataset is to predict the flexion of individual fingers from\r\nsignals recorded from the surface of the brain (electrocorticography (ECoG)). This data set contains\r\nbrain signals from three subjects, as well as the time courses of the flexion of each of five fingers.\r\nThe task in this competition is to use the provided flexion information in order to predict finger\r\nflexion for a provided test set. The performance of the classifier will be evaluated by calculating the\r\naverage correlation coefficient r between actual and predicted finger flexion.\r\n\r\n\r\nECoG data during individual flexions of the five fingers; movements acquired with a data glove.\r\n[48 - 64 ECoG channels (0.15-200Hz), 1000Hz sampling rate, 3 subjects]","description_withheld":null,"homepage":"https://www.bbci.de/competition/iv/desc_4.pdf","introduced_date":"2008-06-11","introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Biomedical","url":"/datasets/modality/biomedical"},{"name":"Medical","url":"/datasets/modality/medical"},{"name":"Time series","url":"/datasets/modality/time-series"}],"tasks":[{"name":"Brain Decoding","url":"/task/brain-decoding","datasets_with_task":"/datasets/task/brain-decoding"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["BCI Competition IV: ECoG to Finger Movements"],"data_loaders":[],"num_papers_in_archive":7,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/brain-decoding-on-bci-competition-iv-ecog-to","task":"Brain Decoding","dataset_variant":"BCI Competition IV: ECoG to Finger Movements","rows":7,"metrics":["Pearson Correlation"],"first_row_in_archive_order":{"model":"FingerFlex","paper":"/paper/fingerflex-inferring-finger-trajectories-from","metrics":{"Pearson Correlation":"0.67"},"code_links":[{"title":"Irautak/FingerFlex","url":"https://github.com/Irautak/FingerFlex"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/fingerflex-inferring-finger-trajectories-from","title":"FingerFlex: Inferring Finger Trajectories from ECoG signals","date":"2022-10-23","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/fast-and-accurate-decoding-of-finger","title":"Fast and accurate decoding of finger movements from ECoG through Riemannian features and modern machine learning techniques","date":"2022-02-25","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/interpreting-wide-band-neural-activity-using","title":"Interpreting wide-band neural activity using convolutional neural networks","date":"2021-08-02","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/decoding-and-interpreting-cortical-signals","title":"Decoding and interpreting cortical signals with a compact convolutional neural network","date":"2021-03-02","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/decoding-of-finger-trajectory-from-ecog-using","title":"Decoding of finger trajectory from ECoG using deep learning","date":"2018-02-28","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/decoding-finger-flexion-from-band-specific","title":"Decoding finger flexion from band-specific ECoG signals in humans","date":"2012-06-28","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/decoding-finger-movements-from-ecog-signals","title":"Decoding finger movements from ECoG signals using switching linear models","date":"2012-03-06","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+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."}