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Data are preprocessed and re-referenced to earlobe.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000138","concept_doi":"10.82901/nemar.nm000138","latest_version_doi":"10.82901/nemar.nm000138.v1.0.2","created_at":"2026-03-17 21:57:16","updated_at":"2026-08-18 18:12:58","zenodo_concept_id":"19632008","is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"Alex Motor Imagery dataset\",\n  \"description\": \"Motor imagery EEG dataset from 8 healthy subjects performing cue-based motor imagination tasks (right hand, feet, rest). Recorded at 512 Hz with 16 electrodes using g.tec g.USBamp amplifier. Dataset comprises 60 trials (20 per class, 3 seconds each) and was developed to validate robust asynchronous EEG-based brain-computer interface control. Data are preprocessed and re-referenced to earlobe.\",\n  \"methods_description\": \"Cue-based motor imagery paradigm without feedback (Step B of Brain Switch campaign). EEG recorded at 512 Hz sampling rate using 16 active electrodes (Fpz, F7, F3, Fz, F4, F8, T7, C3, Cz, C4, T8, P7, P3, Pz, P4, P8) in standard 1005 montage with g.tec g.USBamp hardware and Matlab/Simulink software. Reference electrode placed on earlobe. 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Subjects perform 20 imagined movements per class (right hand, feet, rest) following a visual cue, lasting 3 seconds each. Total duration approximately 10 minutes.\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  feet\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Imagine, Move, Foot\n\n  rest\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Rest\n\n```\n## Paradigm-Specific Parameters\n\n- **Detected paradigm**: motor_imagery\n- **Imagery tasks**: right_hand, feet, rest\n- **Cue duration**: 1.0 s\n- **Imagery duration**: 3.0 s\n\n## Data Structure\n\n- **Trials**: 60\n- **Trials per class**: right_hand=20, feet=20, rest=20\n- **Trials context**: 20 trials per class, 3 second duration each\n\n## Preprocessing\n\n- **Re-reference**: earlobe\n\n## Signal Processing\n\n- **Classifiers**: LDA, SVM, MDM, Riemannian, kNN, Naive Bayes, Logistic Regression\n- **Feature extraction**: CSP, FBCSP, ERD, ERS, PSD, Covariance/Riemannian, AR, ICA\n- **Frequency bands**: alpha=[8.0, 12.0] Hz; mu=[8.0, 12.0] Hz\n- **Spatial filters**: CSP, Geodesic filtering\n\n## Cross-Validation\n\n- **Method**: cross-validation\n- **Evaluation type**: within_session\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**: Research\n\n## Documentation\n\n- **Description**: Motor imagery dataset from the PhD dissertation of A. Barachant. Contains EEG recordings from 8 subjects performing motor imagination tasks (right hand, feet, or rest). Used to validate robust control of an effector via asynchronous EEG-based brain-machine interface.\n- **DOI**: 10.5281/zenodo.806022\n- **Associated paper DOI**: tel-01196752v1\n- **License**: CC-BY-SA-4.0\n- **Investigators**: Alexandre Barachant\n- **Senior author**: Alexandre Barachant\n- **Contact**: alexandre.barachant@gmail.com\n- **Institution**: Université de Grenoble\n- **Department**: Laboratoire Électronique et système pour la santé CEA-LETI\n- **Address**: CEA-LETI Grenoble, France\n- **Country**: France\n- **Repository**: Zenodo\n- **Data URL**: https://zenodo.org/record/806023\n- **Publication year**: 2012\n- **Keywords**: brain-computer interface, motor imagery, EEG, Riemannian geometry, asynchronous BCI, brain-switch, covariance matrices, Common Spatial Pattern\n\n## Abstract\n\nMotor imagery dataset from the PhD thesis on robust control of an effector via asynchronous EEG brain-machine interface (Barachant, 2012). This shared dataset corresponds to Step B (cue-based imagery without feedback) of the Brain Switch campaign. Contains recordings from 8 subjects performing 3 motor imagery tasks (right hand, feet, rest) with 20 trials per class.\n\n## Methodology\n\nCue-based paradigm without feedback (Step B of Brain Switch campaign). EEG recorded at 512 Hz with 16 active electrodes using a g.tec g.USBamp amplifier. Reference electrode placed on the ear. Subjects performed imagined movements following visual cues: right hand, feet, and rest, 20 trials per class, 3 seconds each. Recorded in standard office conditions (not shielded laboratory). Software: Matlab/Simulink with g.tec drivers.\n\n## References\n\nBarachant, A., 2012. Commande robuste d'un effecteur par une interface cerveau machine EEG asynchrone (Doctoral dissertation, Université de Grenoble). https://tel.archives-ouvertes.fr/tel-01196752\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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