{"dataset":{"id":"204","dataset_id":"nm000172","name":"High-gamma dataset described in Schirrmeister et al. 2017","description":"A high-gamma EEG dataset comprising 14 healthy subjects performing motor imagery tasks (left hand, right hand, feet, and rest) recorded at 500 Hz with 128 channels. This is a BIDS-formatted derivative of the original dataset described in Schirrmeister et al. 2017, which was used to develop and validate deep convolutional neural networks for end-to-end EEG decoding. The derivative demonstrates that deep learning approaches can match or exceed traditional feature-based methods (FBCSP) while learning interpretable spectral power modulations in alpha, beta, and high-gamma frequency bands.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000172","concept_doi":"10.82901/nemar.nm000172","latest_version_doi":"10.82901/nemar.nm000172.v1.0.3","created_at":"2026-03-23 14:32:06","updated_at":"2026-08-18 18:16:05","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"High-gamma dataset described in Schirrmeister et al. 2017\",\n  \"description\": \"A high-gamma EEG dataset comprising 14 healthy subjects performing motor imagery tasks (left hand, right hand, feet, and rest) recorded at 500 Hz with 128 channels. This is a BIDS-formatted derivative of the original dataset described in Schirrmeister et al. 2017, which was used to develop and validate deep convolutional neural networks for end-to-end EEG decoding. The derivative demonstrates that deep learning approaches can match or exceed traditional feature-based methods (FBCSP) while learning interpretable spectral power modulations in alpha, beta, and high-gamma frequency bands.\",\n  \"methods_description\": \"EEG data were acquired at 500 Hz sampling rate using 128 channels arranged in the standard 1005 montage. Subjects performed four motor imagery tasks (right hand sequential finger-tapping, left hand sequential finger-tapping, feet repetitive toe clenching, and rest) cued by visual arrows on a black background. Each trial lasted 4 seconds with 3-4 seconds inter-trial intervals. Data were collected in 13 runs per subject with 80 trials per run in pseudo-randomized order. Deep ConvNets, Shallow ConvNets, ResNets, and FBCSP with LDA classifiers were evaluated using within-subject cross-validation with holdout evaluation.\",\n  \"license\": \"CC-BY-4.0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"Robin Tibor Schirrmeister\": {\n      \"orcid\": \"0000-0002-5518-7445\",\n      \"affiliations\": [\n        {\n          \"name\": \"Translational Neurotechnology Lab, Epilepsy Center, Medical Center - University of Freiburg, Engelberger Str. 21; Freiburg 79106 Germany\"\n        },\n        {\n          \"name\": \"BrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Georges-Köhler-Allee 79; Freiburg 79110 Germany\"\n        }\n      ]\n    },\n    \"Jost Tobias Springenberg\": {},\n    \"Lukas Dominique Josef Fiederer\": {\n      \"orcid\": \"0000-0003-1803-9694\",\n      \"affiliations\": [\n        {\n          \"name\": \"Translational Neurotechnology Lab, Epilepsy Center, Medical Center - University of Freiburg, Engelberger Str. 21; Freiburg 79106 Germany\"\n        },\n        {\n          \"name\": \"BrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Georges-Köhler-Allee 79; Freiburg 79110 Germany\"\n        },\n        {\n          \"name\": \"Neurobiology and Biophysics; Faculty of Biology, University of Freiburg, Hansastr. 9a; Freiburg 79104 Germany\"\n        }\n      ]\n    },\n    \"Martin Glasstetter\": {},\n    \"Katharina Eggensperger\": {},\n    \"Michael Tangermann\": {},\n    \"Frank Hutter\": {},\n    \"Wolfram Burgard\": {},\n    \"Tonio Ball\": {\n      \"orcid\": \"0000-0002-4993-466X\",\n      \"affiliations\": [\n        {\n          \"name\": \"Translational Neurotechnology Lab, Epilepsy Center, Medical Center - University of Freiburg, Engelberger Str. 21; Freiburg 79106 Germany\"\n        },\n        {\n          \"name\": \"BrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Georges-Köhler-Allee 79; Freiburg 79110 Germany\"\n        }\n 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\"Dataset\",\n  \"resource_type_specific\": \"EEG Dataset\",\n  \"modalities\": [\n    \"eeg\"\n  ],\n  \"sizes\": [\n    \"33.6 GB (59 files)\"\n  ],\n  \"formats\": [\n    \".bdf\",\n    \".edf\",\n    \".html\",\n    \".json\",\n    \".md\",\n    \".tsv\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"source_hash\": \"4e4881a0249e3920ff960eedf7a05775ce39d3494d4db6d7d25a499d23ae5bc8\"\n}","last_activity_at":"2026-08-16 13:28:21","source":null,"source_id":null,"subject_count":14,"modalities":"eeg","age_min":27.2,"age_max":27.2,"file_size":33606135475,"total_files":279,"tasks":"imagery","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Robin Tibor Schirrmeister, Jost Tobias Springenberg, Lukas Dominique Josef Fiederer, Martin Glasstetter, Katharina Eggensperger, Michael Tangermann, Frank Hutter, Wolfram Burgard, Tonio Ball","license":"CC-BY-4.0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000172-blue)](https://doi.org/10.82901/nemar.nm000172)\n\n# High-gamma dataset described in Schirrmeister et al. 2017\n\nHigh-gamma dataset described in Schirrmeister et al. 2017.\n\n## Dataset Overview\n\n- **Code**: Schirrmeister2017\n- **Paradigm**: imagery\n- **DOI**: 10.1002/hbm.23730\n- **Subjects**: 14\n- **Sessions per subject**: 1\n- **Events**: right_hand=1, left_hand=2, rest=3, feet=4\n- **Trial interval**: [0, 4] s\n- **Runs per session**: 2\n- **File format**: EDF\n\n## Acquisition\n\n- **Sampling rate**: 500.0 Hz\n- **Number of channels**: 128\n- **Channel types**: eeg=128\n- **Channel names**: Fp1, Fp2, Fpz, F7, F3, Fz, F4, F8, FC5, FC1, FC2, FC6, M1, T7, C3, Cz, C4, T8, M2, CP5, CP1, CP2, CP6, P7, P3, Pz, P4, P8, POz, O1, Oz, O2, AF7, AF3, AF4, AF8, F5, F1, F2, F6, FC3, FCz, FC4, C5, C1, C2, C6, CP3, CPz, CP4, P5, P1, P2, P6, PO5, PO3, PO4, PO6, FT7, FT8, TP7, TP8, PO7, PO8, FT9, FT10, TPP9h, TPP10h, PO9, PO10, P9, P10, AFF1, AFz, AFF2, FFC5h, FFC3h, FFC4h, FFC6h, FCC5h, FCC3h, FCC4h, FCC6h, CCP5h, CCP3h, CCP4h, CCP6h, CPP5h, CPP3h, CPP4h, CPP6h, PPO1, PPO2, I1, Iz, I2, AFp3h, AFp4h, AFF5h, AFF6h, FFT7h, FFC1h, FFC2h, FFT8h, FTT9h, FTT7h, FCC1h, FCC2h, FTT8h, FTT10h, TTP7h, CCP1h, CCP2h, TTP8h, TPP7h, CPP1h, CPP2h, TPP8h, PPO9h, PPO5h, PPO6h, PPO10h, POO9h, POO3h, POO4h, POO10h, OI1h, OI2h\n- **Montage**: standard_1005\n- **Software**: BCI2000\n- **Sensor type**: EEG\n- **Line frequency**: 50.0 Hz\n\n## Participants\n\n- **Number of subjects**: 14\n- **Health status**: healthy\n- **Age**: mean=27.2, std=3.6\n- **Gender distribution**: female=6, male=8\n- **Handedness**: {'right': 12, 'left': 2}\n\n## Experimental Protocol\n\n- **Paradigm**: imagery\n- **Number of classes**: 4\n- **Class labels**: right_hand, left_hand, rest, feet\n- **Trial duration**: 4.0 s\n- **Study design**: Executed movements including left hand (sequential finger-tapping), right hand (sequential finger-tapping), feet (repetitive toe clenching), and rest conditions\n- **Stimulus type**: visual\n- **Stimulus modalities**: visual\n- **Primary modality**: visual\n- **Synchronicity**: cue-based\n- **Mode**: offline\n- **Training/test split**: True\n- **Instructions**: Subjects performed repetitive movements at their own pace when arrow was showing\n- **Stimulus presentation**: type=gray arrow on black background, direction_mapping=downward=feet, leftward=left_hand, rightward=right_hand, upward=rest\n\n## HED Event Annotations\n\nSchema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n\n```\n  right_hand\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Imagine\n          ├─ Move\n          └─ Right, Hand\n\n  left_hand\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Imagine\n          ├─ Move\n          └─ Left, Hand\n\n  rest\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Rest\n\n  feet\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Imagine, Move, Foot\n\n```\n## Paradigm-Specific Parameters\n\n- **Detected paradigm**: motor_imagery\n- **Imagery tasks**: left_hand_finger_tapping, right_hand_finger_tapping, feet_toe_clenching, rest\n\n## Data Structure\n\n- **Trials**: {'total_per_subject': 963, 'training_set': 880, 'test_set': 160}\n- **Trials per class**: per_class_per_subject=260\n- **Blocks per session**: 13\n- **Trials context**: 13 runs per subject, 80 trials per run (4 seconds each), 3-4 seconds inter-trial interval, pseudo-randomized presentation with all 4 classes shown every 4 trials\n\n## Signal Processing\n\n- **Classifiers**: Deep ConvNet, Shallow ConvNet, ResNet, FBCSP with LDA\n- **Feature extraction**: FBCSP, CSP, Bandpower, Spectral power modulations\n- **Frequency bands**: alpha=[7.0, 13.0] Hz; beta=[13.0, 30.0] Hz; gamma=[30.0, 100.0] Hz\n- **Spatial filters**: CSP\n\n## Cross-Validation\n\n- **Method**: holdout\n- **Evaluation type**: within_subject\n\n## Performance (Original Study)\n\n- **Fbcsp Accuracy**: 91.2\n- **Deep Convnet Accuracy**: 89.3\n- **Shallow Convnet Accuracy**: 92.5\n\n## BCI Application\n\n- **Applications**: motor_control\n- **Environment**: laboratory\n- **Online feedback**: False\n\n## Tags\n\n- **Pathology**: Healthy\n- **Modality**: Motor\n- **Type**: Motor Imagery, Motor Execution\n\n## Documentation\n\n- **DOI**: 10.1002/hbm.23730\n- **License**: CC-BY-4.0\n- **Investigators**: Robin Tibor Schirrmeister, Jost Tobias Springenberg, Lukas Dominique Josef Fiederer, Martin Glasstetter, Katharina Eggensperger, Michael Tangermann, Frank Hutter, Wolfram Burgard, Tonio Ball\n- **Senior author**: Tonio Ball\n- **Contact**: robin.schirrmeister@uniklinik-freiburg.de\n- **Institution**: University of Freiburg\n- **Department**: Translational Neurotechnology Lab, Epilepsy Center, Medical Center\n- **Address**: Engelberger Str. 21, Freiburg 79106, Germany\n- **Country**: DE\n- **Repository**: GitHub\n- **Data URL**: https://web.gin.g-node.org/robintibor/high-gamma-dataset/\n- **Publication year**: 2017\n- **Funding**: BrainLinks-BrainTools Cluster of Excellence (DFG) EXC1086; Federal Ministry of Education and Research (BMBF) Motor-BIC 13GW0053D\n- **Ethics approval**: Approved by the ethical committee of the University of Freiburg\n- **Acknowledgements**: Funded by BrainLinks-BrainTools Cluster of Excellence (DFG, EXC1086) and the Federal Ministry of Education and Research (BMBF, Motor-BIC 13GW0053D).\n- **How to acknowledge**: Please cite: Schirrmeister et al. (2017). Deep learning with convolutional neural networks for EEG decoding and visualization. Human Brain Mapping, 38(11), 5391-5420. https://doi.org/10.1002/hbm.23730\n- **Keywords**: electroencephalography, EEG analysis, machine learning, end-to-end learning, brain-machine interface, brain-computer interface, model interpretability, brain mapping\n\n## Abstract\n\nDeep learning with convolutional neural networks (deep ConvNets) has revolutionized computer vision through end-to-end learning. This study investigates deep ConvNets for end-to-end EEG decoding of imagined or executed movements from raw EEG. Results show that recent advances including batch normalization and exponential linear units, together with a cropped training strategy, boosted decoding performance to match or exceed FBCSP (82.1% FBCSP vs 84.0% deep ConvNets). Novel visualization methods demonstrated that ConvNets learned to use spectral power modulations in alpha, beta, and high gamma frequencies with meaningful spatial distributions.\n\n## Methodology\n\nEnd-to-end deep learning approach comparing shallow ConvNets, deep ConvNets, and ResNets against FBCSP baseline. Evaluated design choices including batch normalization, exponential linear units, dropout, and cropped training strategies. Novel visualization techniques developed to understand learned features and verify that ConvNets use spectral power modulations in task-relevant frequency bands.\n\n## References\n\nSchirrmeister, Robin Tibor, et al. \"Deep learning with convolutional neural networks for EEG decoding and visualization.\" Human brain mapping 38.11 (2017): 5391-5420.\nAppelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Hochenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896\n\nPernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. 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