{"url":"/dataset/bnci-2015-001-motor-imagery-dataset-1","name":"BNCI 2015-001 Motor Imagery dataset","full_name":null,"description_markdown":"**Dataset description**\r\n\r\nWe acquired the EEG from three Laplacian derivations, 3.5 cm (center-to-\r\ncenter) around the electrode positions (according to International 10-20\r\nSystem of Electrode Placement) C3 (FC3, C5, CP3 and C1), Cz (FCz, C1, CPz\r\nand C2) and C4 (FC4, C2, CP4 and C6).  The acquisition hardware was a\r\ng.GAMMAsys active electrode system along with a g.USBamp amplifier (g.tec,\r\nGuger Tech- nologies OEG, Graz, Austria).  The system sampled at 512 Hz,\r\nwith a bandpass filter between 0.5 and 100 Hz and a notch filter at 50 Hz.\r\nThe order of the channels in the data is FC3, FCz, FC4, C5, C3, C1, Cz, C2,\r\nC4, C6, CP3, CPz, CP4.\r\n\r\nThe task for the user was to perform sustained right hand versus both feet\r\nmovement imagery starting from the cue (second 3) to the end of the cross\r\nperiod (sec- ond 8).  A trial started with 3 s of reference period,\r\nfollowed by a brisk audible cue and a visual cue (arrow right for right\r\nhand, arrow down for both feet) from second 3 to 4.25.\r\nThe activity period, where the users received feedback, lasted from\r\nsecond 4 to 8. There was a random 2 to 3 s pause between the trials.\r\n\r\nReferences\r\n----------\r\n\r\n[1] J. Faller, C. Vidaurre, T. Solis-Escalante, C. Neuper and R.\r\n       Scherer (2012). Autocalibration and recurrent adaptation: Towards a\r\n       plug and play online ERD- BCI.  IEEE Transactions on Neural Systems\r\n       and Rehabilitation Engineering, 20(3), 313-319.","description_withheld":null,"homepage":"http://bnci-horizon-2020.eu/database/data-sets","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":{"name":"Open access","url":"http://bnci-horizon-2020.eu/database/data-sets"},"modalities":[{"name":"EEG","url":"/datasets/modality/eeg"}],"tasks":[{"name":"Motor Imagery Decoding (left-hand vs right-hand)","url":"/task/motor-imagery-decoding-left-hand-vs-right","datasets_with_task":"/datasets/task/motor-imagery-decoding-left-hand-vs-right"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["BNCI 2015-001 Motor Imagery dataset"],"data_loaders":[],"num_papers_in_archive":0,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"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."}