{"url":"/dataset/high-gamma-dataset-discribed-in-schirrmeister","name":"High-gamma dataset discribed in Schirrmeister et al. 2017","full_name":"EEG High-Gamma Dataset","description_markdown":"High-gamma dataset discribed in Schirrmeister et al. 2017\r\n\r\nOur “High-Gamma Dataset” is a 128-electrode dataset (of which we later only use\r\n44 sensors covering the motor cortex, (see Section 2.7.1), obtained from 14\r\nhealthy subjects (6 female, 2 left-handed, age 27.2 ± 3.6 (mean ± std)) with\r\nroughly 1000 (963.1 ± 150.9, mean ± std) four-second trials of executed\r\nmovements divided into 13 runs per subject.  The four classes of movements were\r\nmovements of either the left hand, the right hand, both feet, and rest (no\r\nmovement, but same type of visual cue as for the other classes).  The training\r\nset consists of the approx.  880 trials of all runs except the last two runs,\r\nthe test set of the approx.  160 trials of the last 2 runs.  This dataset was\r\nacquired in an EEG lab optimized for non-invasive detection of high- frequency\r\nmovement-related EEG components (Ball et al., 2008; Darvas et al., 2010).\r\n\r\nDepending on the direction of a gray arrow that was shown on black back-\r\nground, the subjects had to repetitively clench their toes (downward arrow),\r\nperform sequential finger-tapping of their left (leftward arrow) or right\r\n(rightward arrow) hand, or relax (upward arrow).  The movements were selected\r\nto require little proximal muscular activity while still being complex enough\r\nto keep subjects in- volved.  Within the 4-s trials, the subjects performed the\r\nrepetitive movements at their own pace, which had to be maintained as long as\r\nthe arrow was showing.  Per run, 80 arrows were displayed for 4 s each, with 3\r\nto 4 s of continuous random inter-trial interval.  The order of presentation\r\nwas pseudo-randomized, with all four arrows being shown every four trials.\r\nIdeally 13 runs were performed to collect 260 trials of each movement and rest.\r\nThe stimuli were presented and the data recorded with BCI2000 (Schalk et al.,\r\n2004).  The experiment was approved by the ethical committee of the University\r\nof Freiburg.\r\n\r\n## References\r\n\r\n[1] Schirrmeister, Robin Tibor, et al. \"Deep learning with convolutional\r\nneural networks for EEG decoding and visualization.\" Human brain mapping 38.11\r\n(2017): 5391-5420.","description_withheld":null,"homepage":"https://doi.org/10.1002/hbm.23730","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"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":[],"variants":["High-gamma dataset discribed in Schirrmeister et al. 2017"],"data_loaders":[{"repo":"https://github.com/NeuroTechX/moabb","url":"http://moabb.neurotechx.com/docs/generated/moabb.datasets.Schirrmeister2017.html","frameworks":[]}],"num_papers_in_archive":2,"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."}