{"url":"/dataset/shin2017a-moabb","name":"Shin2017A MOABB","full_name":"Motor Imagey Dataset from Shin et al 2017.","description_markdown":"","description_withheld":null,"homepage":"http://moabb.neurotechx.com/docs/generated/moabb.datasets.Shin2017A.html","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[{"name":"Within-Session Motor Imagery (left hand vs. right hand)","url":"/task/within-session-motor-imagery-left-hand-vs","datasets_with_task":"/datasets/task/within-session-motor-imagery-left-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-left-hand-vs-7","task":"Within-Session Motor Imagery (left hand vs. right hand)","dataset_variant":"Shin2017A MOABB","rows":19,"metrics":["AUC-ROC","training time (s)","CO2 Emission (g)"],"first_row_in_archive_order":{"model":"CSP + LDA","paper":"/paper/the-largest-eeg-based-bci-reproducibility-1","metrics":{"AUC-ROC":"72.29885057471265","CO2 Emission (g)":"0.0031280651400344825","training time (s)":"6.032976467126437"},"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":19,"code_links":1,"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."}