{"dataset":{"id":"279","dataset_id":"nm000246","name":"Multi-day MI-BCI dataset (WBCIC-SHU) from Yang et al 2025","description":"A multi-day motor imagery brain-computer interface (MI-BCI) dataset comprising EEG recordings from 51 healthy subjects performing left and right hand motor imagery tasks across three sessions. The dataset includes 59-channel EEG data (plus 1 ECG and 4 EOG channels) sampled at 1000 Hz with standardized 10-05 electrode montage, totaling 39,600 trials with visual and auditory cues. This resource supports the development and benchmarking of BCI algorithms for motor control applications.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000246","concept_doi":"10.82901/nemar.nm000246","latest_version_doi":"10.82901/nemar.nm000246.v1.0.0","created_at":"2026-03-26 00:43:43","updated_at":"2026-07-10 22:00:48","zenodo_concept_id":"20521460","is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"enriched\",\n  \"authors\": {\n    \"Banghua Yang\": {},\n    \"Fenqi Rong\": {},\n    \"Yunlong Xie\": {},\n    \"Du Li\": {},\n    \"Jiayang Zhang\": {},\n    \"Fu Li\": {},\n    \"Guangming Shi\": {},\n    \"Xiaorong Gao\": {}\n  },\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.1038/s41597-025-04826-y\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/nm000246\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataexplorer/detail?dataset_id=nm000246\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.21105/joss.01896\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.1038/s41597-019-0104-8\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    }\n  ],\n  \"title\": \"Multi-day MI-BCI dataset (WBCIC-SHU) from Yang et al 2025\",\n  \"license\": \"CC-BY-4.0\",\n  \"dataset_type\": \"derivative\",\n  \"resource_type_general\": \"Dataset\",\n  \"modalities\": [\n    \"eeg\"\n  ],\n  \"resource_type_specific\": \"EEG Dataset\",\n  \"sizes\": [\n    \"62.7 GB (154 files)\"\n  ],\n  \"formats\": [\n    \".bdf\",\n    \".json\",\n    \".md\",\n    \".tsv\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"description\": \"A multi-day motor imagery brain-computer interface (MI-BCI) dataset comprising EEG recordings from 51 healthy subjects performing left and right hand motor imagery tasks across three sessions. The dataset includes 59-channel EEG data (plus 1 ECG and 4 EOG channels) sampled at 1000 Hz with standardized 10-05 electrode montage, totaling 39,600 trials with visual and auditory cues. This resource supports the development and benchmarking of BCI algorithms for motor control applications.\",\n  \"methods_description\": \"EEG data were acquired using a Neuracle NeuSen W system with 59 Ag/AgCl electrodes arranged in the standard 10-05 montage, sampled at 1000 Hz with 50 Hz line frequency filtering. Subjects performed motor imagery tasks cued by visual and auditory stimuli with 1.5 s cue duration followed by 4.0 s imagery period. Three sessions per subject were conducted on different days, with 200 trials per session for the 2-class paradigm (left/right hand) and 300 trials for the 3-class paradigm (left hand, right hand, foot).\",\n  \"keywords\": [\n    {\n      \"term\": \"motor imagery\"\n    },\n    {\n      \"term\": \"Brain-Computer Interfaces\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D062207\"\n    },\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"Electroencephalography\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D004569\"\n    },\n    {\n      \"term\": \"motor control\"\n    },\n    {\n      \"term\": \"signal processing\"\n    },\n    {\n      \"term\": \"BCI paradigms\"\n    },\n    {\n      \"term\": \"multi-day\"\n    },\n    {\n      \"term\": \"longitudinal\"\n    },\n    {\n      \"term\": \"Healthy\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"Unknown\",\n      \"award_number\": \"\"\n    },\n    {\n      \"funder_name\": \"No funding received\",\n      \"award_number\": \"\"\n    }\n  ],\n  \"source_hash\": \"7a653e843645ab938666832ae8fe8ef30465f1f1b9e1ad0a4b9c70e5e06ec25a\"\n}","last_activity_at":"2026-03-26 00:44:03","source":null,"source_id":null,"subject_count":51,"modalities":"eeg","age_min":29,"age_max":29,"file_size":62731200306,"total_files":154,"tasks":"imagery","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Banghua Yang, Fenqi Rong, Yunlong Xie, Du Li, Jiayang Zhang, Fu Li, Guangming Shi, Xiaorong Gao","license":"CC-BY-4.0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000246-blue)](https://doi.org/10.82901/nemar.nm000246)\n\n# Multi-day MI-BCI dataset (WBCIC-SHU) from Yang et al 2025\n\nMulti-day MI-BCI dataset (WBCIC-SHU) from Yang et al 2025.\n\n## Dataset Overview\n\n- **Code**: Yang2025\n- **Paradigm**: imagery\n- **DOI**: 10.1038/s41597-025-04826-y\n- **Subjects**: 51\n- **Sessions per subject**: 3\n- **Events**: left_hand=1, right_hand=2\n- **Trial interval**: [1.5, 5.5] s\n- **File format**: BDF\n\n## Acquisition\n\n- **Sampling rate**: 1000.0 Hz\n- **Number of channels**: 59\n- **Channel types**: eeg=59, ecg=1, eog=4\n- **Channel names**: Fpz, Fp1, Fp2, AF3, AF4, AF7, AF8, Fz, F1, F2, F3, F4, F5, F6, F7, F8, FCz, FC1, FC2, FC3, FC4, FC5, FC6, FT7, FT8, Cz, C1, C2, C3, C4, C5, C6, T7, T8, CP1, CP2, CP3, CP4, CP5, CP6, TP7, TP8, Pz, P3, P4, P5, P6, P7, P8, POz, PO3, PO4, PO5, PO6, PO7, PO8, Oz, O1, O2\n- **Montage**: standard_1005\n- **Hardware**: Neuracle NeuSen W\n- **Sensor type**: Ag/AgCl\n- **Line frequency**: 50.0 Hz\n- **Online filters**: {}\n\n## Participants\n\n- **Number of subjects**: 51\n- **Health status**: healthy\n- **Age**: min=17.0, max=30.0\n- **Gender distribution**: female=18, male=44\n- **Handedness**: right-handed\n- **BCI experience**: naive\n- **Species**: human\n\n## Experimental Protocol\n\n- **Paradigm**: imagery\n- **Number of classes**: 2\n- **Class labels**: left_hand, right_hand\n- **Trial duration**: 7.5 s\n- **Study design**: Multi-day MI-BCI: 2C (left/right hand, 51 subj) and 3C (left hand, right hand, foot-hooking, 11 subj). 3 sessions per subject on different days.\n- **Feedback type**: none\n- **Stimulus type**: video cues\n- **Stimulus modalities**: visual, auditory\n- **Primary modality**: visual\n- **Synchronicity**: synchronous\n- **Mode**: offline\n\n## HED Event Annotations\n\nSchema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n\n```\n  left_hand\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Imagine\n          ├─ Move\n          └─ Left, Hand\n\n  right_hand\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Imagine\n          ├─ Move\n          └─ Right, Hand\n\n```\n## Paradigm-Specific Parameters\n\n- **Detected paradigm**: motor_imagery\n- **Imagery tasks**: left_hand, right_hand, feet\n- **Cue duration**: 1.5 s\n- **Imagery duration**: 4.0 s\n\n## Data Structure\n\n- **Trials**: 39600\n- **Trials context**: 51 subjects x 3 sessions x 200 trials (2C) + 11 subjects x 3 sessions x 300 trials (3C) = 39600\n\n## Signal Processing\n\n- **Classifiers**: CSP+SVM, FBCSP+SVM, EEGNet, deepConvNet, FBCNet\n- **Feature extraction**: CSP, FBCSP\n- **Frequency bands**: bandpass=[0.5, 40.0] Hz\n- **Spatial filters**: CSP, FBCSP\n\n## Cross-Validation\n\n- **Method**: 10-fold\n- **Folds**: 10\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- **DOI**: 10.1038/s41597-025-04826-y\n- **License**: CC-BY-4.0\n- **Investigators**: Banghua Yang, Fenqi Rong, Yunlong Xie, Du Li, Jiayang Zhang, Fu Li, Guangming Shi, Xiaorong Gao\n- **Institution**: Shanghai University\n- **Country**: CN\n- **Data URL**: https://plus.figshare.com/articles/dataset/22671172\n- **Publication year**: 2025\n\n## References\n\nYang, B., Rong, F., Xie, Y., et al. (2025). A multi-day and high-quality EEG dataset for motor imagery brain-computer interface. Scientific Data, 12, 488. https://doi.org/10.1038/s41597-025-04826-y\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. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8\n\n---\nGenerated by MOABB 1.5.0 (Mother of All BCI Benchmarks)\nhttps://github.com/NeuroTechX/moabb\n","bids_version":"1.9.0","sessions_count":3,"publish_date":"2026-03-26 00:43:43","embedding_dirty":0,"license_tier":"attribution","zarr_status":"ready","zarr_converted_at":"2026-09-04 09:19:02","zarr_store_count":153,"zarr_index_etag":"15c6a66f14ec32b72544e9432e773451","zarr_source_commit":"0365fc101278ce55fb047aa1fc3df2dd72bc9f62","archive_status":"ready","archive_size":45926365150,"archive_retry_count":0,"records_status":null,"archive_skip_reason":null,"zarr_errors":0,"zarr_failure_count":0,"zarr_deterministic":0,"zarr_failed_at":null,"num_dataset_citations":0,"num_datapaper_citations":34,"n_channels":null,"electrode_system":null,"has_hed":1,"hed_version":"8.4.0","is_exemplar":0,"bytes_present":null,"data_complete":null,"withdrawn_at":null,"withdrawn_reason":null,"archive_complete":null,"archive_absent_files":null,"archive_declared_files":null,"zarr_pool_breaks":0,"total_recording_duration":354334,"recording_duration_min":2142,"recording_duration_max":3261,"recording_count":153,"recordings_unavailable":0,"recordings_measured":153,"channel_count_min":59,"channel_count_max":59,"sampling_frequency":1000,"power_line_frequency":50,"eeg_reference":null,"placement_scheme":"10-05 system","sweep_stamps":"{\"enrichment_updated_at\":\"2026-06-03 06:05:32\",\"metadata_updated_at\":\"2026-06-03 06:06:13\",\"archive_checked_at\":\"2026-06-05 01:33:12\",\"zarr_checked_at\":\"2026-06-07 17:58:35\",\"records_checked_at\":null,\"citations_updated_at\":\"2026-09-08 03:00:48\",\"channel_montage_checked_at\":\"2026-06-28 23:00:15\",\"hed_checked_at\":\"2026-06-30 07:32:41\",\"data_checked_at\":null,\"availability_report_at\":\"2026-07-23 01:09:22\",\"signal_defaults_at\":\"2026-09-02 11:48:38\",\"recording_stats_at\":\"2026-09-05 03:01:50\"}","participants":51,"num_citations":34,"latest_version":"v1.0.0","zarr_verify_status":null,"zarr_verified_at":null,"owner_username":"bruaristimunha","owner_github":"bruAristimunha","file_size_formatted":"58.42 GB","zarr_data_failures":null,"zarr_index_url":"https://zarr.nemar.org/nm000246/zarr/index.json","attestation_deposit_type":null,"attestation_key_status":null,"attestation_deidentified":null,"attestation_no_duplicate":null,"attestation_upstream_source":null,"attestation_accepted_at":null}}