{"dataset":{"id":"178","dataset_id":"nm000145","name":"Munich Motor Imagery dataset","description":"The Munich Motor Imagery dataset comprises electroencephalographic recordings from 10 healthy subjects performing two-class motor imagery tasks (left and right hand) cued by visual arrow stimuli. The dataset includes 128-channel EEG data sampled at 500 Hz with preprocessed signals suitable for brain-computer interface research and benchmarking of spatial filtering and classification methods.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000145","concept_doi":"10.82901/nemar.nm000145","latest_version_doi":"10.82901/nemar.nm000145.v1.0.2","created_at":"2026-03-22 17:19:23","updated_at":"2026-08-18 18:12:57","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"Munich Motor Imagery dataset\",\n  \"description\": \"The Munich Motor Imagery dataset comprises electroencephalographic recordings from 10 healthy subjects performing two-class motor imagery tasks (left and right hand) cued by visual arrow stimuli. The dataset includes 128-channel EEG data sampled at 500 Hz with preprocessed signals suitable for brain-computer interface research and benchmarking of spatial filtering and classification methods.\",\n  \"methods_description\": \"EEG data were acquired using a 128-channel BrainAmp system at 500 Hz sampling rate with reference at Cz and standard 10-20 montage. Subjects performed motor imagery tasks lasting 7 seconds following visual arrow cues in a shielded room. Data were preprocessed with common average reference re-referencing and no artifact rejection applied.\",\n  \"license\": \"CC-BY-4.0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"Moritz Grosse-Wentrup\": {},\n    \"Christian Liefhold\": {},\n    \"Klaus Gramann\": {},\n    \"Martin Buss\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"Electroencephalography\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D004569\"\n    },\n    {\n      \"term\": \"motor imagery\"\n    },\n    {\n      \"term\": \"brain-computer interfaces\"\n    },\n    {\n      \"term\": \"common spatial patterns\"\n    },\n    {\n      \"term\": \"beamforming\"\n    },\n    {\n      \"term\": \"spatial filtering\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.1109/TBME.2008.2009768\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": 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500.0 Hz\n- **Number of channels**: 128\n- **Channel types**: eeg=128\n- **Channel names**: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128\n- **Montage**: standard_1020\n- **Hardware**: BrainAmp\n- **Reference**: Cz\n- **Line frequency**: 50.0 Hz\n- **Online filters**: {'highpass_time_constant_s': 10}\n- **Impedance threshold**: 10 kOhm\n\n## Participants\n\n- **Number of subjects**: 10\n- **Health status**: healthy\n- **Age**: mean=25.6, std=2.5\n- **Gender distribution**: male=8, female=2\n- **Handedness**: {'right': 8}\n- **BCI experience**: mixed\n- **Species**: human\n\n## Experimental Protocol\n\n- **Paradigm**: imagery\n- **Task type**: motor_imagery\n- **Number of classes**: 2\n- **Class labels**: right_hand, left_hand\n- **Trial duration**: 10 s\n- **Tasks**: motor_imagery\n- **Study design**: two-class motor imagery with arrow cues\n- **Feedback type**: none\n- **Stimulus type**: arrow_cue\n- **Stimulus modalities**: visual\n- **Primary modality**: visual\n- **Synchronicity**: synchronous\n- **Mode**: offline\n- **Instructions**: Subjects were instructed to perform haptic motor imagery of the left or the right hand during display of the arrow, as indicated by the direction of the arrow\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\n    │  ├─ Experimental-stimulus\n    │  ├─ Visual-presentation\n    │  └─ Rightward, Arrow\n    └─ Agent-action\n       └─ Imagine\n          ├─ Move\n          └─ Right, Hand\n\n  left_hand\n    ├─ Sensory-event\n    │  ├─ Experimental-stimulus\n    │  ├─ Visual-presentation\n    │  └─ Leftward, Arrow\n    └─ Agent-action\n       └─ Imagine\n          ├─ Move\n          └─ Left, Hand\n\n```\n## Paradigm-Specific Parameters\n\n- **Detected paradigm**: motor_imagery\n- **Imagery tasks**: left_hand, right_hand\n- **Cue duration**: 7.0 s\n- **Imagery duration**: 7.0 s\n\n## Data Structure\n\n- **Trials**: 150\n- **Trials context**: per_class\n\n## Preprocessing\n\n- **Data state**: preprocessed\n- **Preprocessing applied**: True\n- **Artifact methods**: none\n- **Re-reference**: car\n- **Notes**: No trials were rejected and no artifact correction was performed. Data were re-referenced to common average reference offline.\n\n## Signal Processing\n\n- **Classifiers**: Logistic Regression\n- **Feature extraction**: CSP, Beamforming, Laplacian, Bandpower\n- **Frequency bands**: analyzed=[7.0, 30.0] Hz\n- **Spatial filters**: CSP, Beamforming, Laplacian\n\n## Cross-Validation\n\n- **Method**: bootstrapping\n- **Evaluation type**: within_subject\n\n## BCI Application\n\n- **Applications**: motor_control\n- **Environment**: shielded_room\n- **Online feedback**: False\n\n## Tags\n\n- **Pathology**: Healthy\n- **Modality**: Motor\n- **Type**: Motor\n\n## Documentation\n\n- **DOI**: 10.1109/TBME.2008.2009768\n- **License**: CC-BY-4.0\n- **Investigators**: Moritz Grosse-Wentrup, Christian Liefhold, Klaus Gramann, Martin Buss\n- **Senior author**: Martin Buss\n- **Contact**: moritzgw@ieee.org\n- **Institution**: Technische Universität München\n- **Department**: Institute of Automatic Control Engineering (LSR)\n- **Country**: DE\n- **Repository**: Zenodo\n- **Publication year**: 2009\n- **Keywords**: Beamforming, brain-computer interfaces, common spatial patterns, electroencephalography, motor imagery, spatial filtering\n\n## References\n\nGrosse-Wentrup, Moritz, et al. \"Beamforming in noninvasive brain–computer interfaces.\" IEEE Transactions on Biomedical Engineering 56.4 (2009): 1209-1219.\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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