{"url":"/dataset/bnci2015-004-moabb-1","name":"BNCI2015-004 MOABB","full_name":"BNCI 2015-004 Motor Imagery dataset.","description_markdown":"","description_withheld":null,"homepage":"http://moabb.neurotechx.com/docs/generated/moabb.datasets.BNCI2015_004.html","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[{"name":"Within-Session Motor Imagery (right hand vs. feet)","url":"/task/within-session-motor-imagery-right-hand-vs","datasets_with_task":"/datasets/task/within-session-motor-imagery-right-hand-vs"}],"languages":[],"variants":[],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/within-session-motor-imagery-right-hand-vs-4","task":"Within-Session Motor Imagery (right hand vs. feet)","dataset_variant":"BNCI2015-004 MOABB","rows":16,"metrics":["AUC-ROC","training time (s)","CO2 Emission (g)"],"first_row_in_archive_order":{"model":"TS + SVM","paper":"/paper/the-largest-eeg-based-bci-reproducibility-1","metrics":{"AUC-ROC":"62.55243744444444","CO2 Emission (g)":"0.0030399768388888887","training time (s)":"3.981446488888889"},"code_links":[{"title":"NeuroTechX/moabb","url":"https://github.com/NeuroTechX/moabb"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/the-largest-eeg-based-bci-reproducibility-1","title":"The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark","date":"2024-04-03","rows_on_this_dataset":16,"code_links":1,"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."}