{"dataset":{"id":"383","dataset_id":"nm000348","name":"Yang et al. 2025 — A multi-day and high-quality EEG dataset for motor imagery brain-computer interface","description":"This dataset (WBCIC-SHU) contains multi-day EEG recordings from 51 healthy, right-handed, BCI-naive subjects performing a motor imagery brain-computer interface paradigm across three sessions per subject. Participants imagined left-hand, right-hand, or (for a subset of 11 subjects) foot movements in response to visual and auditory cues, yielding 39,600 trials in total. The dataset is intended for benchmarking motor imagery classification algorithms such as CSP, FBCSP, EEGNet, deepConvNet, and FBCNet, and was converted to BIDS format using MOABB.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000348","concept_doi":"10.82901/nemar.nm000348","latest_version_doi":"10.82901/nemar.nm000348.v1.0.5","created_at":"2026-03-29 06:02:57","updated_at":"2026-08-18 22:52:07","zenodo_concept_id":"20528551","is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"Yang et al. 2025 — A multi-day and high-quality EEG dataset for motor imagery brain-computer interface\",\n  \"description\": \"This dataset (WBCIC-SHU) contains multi-day EEG recordings from 51 healthy, right-handed, BCI-naive subjects performing a motor imagery brain-computer interface paradigm across three sessions per subject. Participants imagined left-hand, right-hand, or (for a subset of 11 subjects) foot movements in response to visual and auditory cues, yielding 39,600 trials in total. The dataset is intended for benchmarking motor imagery classification algorithms such as CSP, FBCSP, EEGNet, deepConvNet, and FBCNet, and was converted to BIDS format using MOABB.\",\n  \"methods_description\": \"EEG was recorded at 1000 Hz using a Neuracle NeuSen W system with 59 Ag/AgCl electrodes (standard_1005 montage) plus ECG and EOG channels, with a line frequency of 50 Hz. Data were stored in BDF format. The motor imagery paradigm involved visual and auditory cues with a 1.5 s cue duration followed by a 4.0 s imagery period, synchronous and offline, with trials of 7.5 s duration.\",\n  \"license\": \"CC-BY-4.0\",\n  \"dataset_type\": \"derivative\",\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  \"keywords\": [\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"motor imagery\"\n    },\n    {\n      \"term\": \"brain-computer interface\"\n    },\n    {\n      \"term\": \"Electroencephalography\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D004569\"\n    },\n    {\n      \"term\": \"multi-day recordings\"\n    },\n    {\n      \"term\": \"BCI benchmarking\"\n    }\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/nm000348\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/nm000348\",\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  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"EEG Dataset\",\n  \"modalities\": [\n    \"eeg\"\n  ],\n  \"sizes\": [\n    \"165.1 GB (743 files)\"\n  ],\n  \"formats\": [\n    \".bdf\",\n    \".csv\",\n    \".gitignore\",\n    \".iml\",\n    \".jpg\",\n    \".json\",\n    \".m\",\n    \".mat\",\n    \".md\",\n    \".py\",\n    \".pyc\",\n    \".rar\",\n    \".tsv\",\n    \".xml\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"source_hash\": \"c72e56ab04addb5b99a78b9dee3074d0281fde42e72ae9896fb7380eb64292c5\"\n}","last_activity_at":"2026-08-18 22:48:42","source":null,"source_id":null,"subject_count":51,"modalities":"eeg","age_min":29,"age_max":29,"file_size":165140160014,"total_files":2237,"tasks":"imagery,motorimagery","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.nm000348-blue)](https://doi.org/10.82901/nemar.nm000348)\n\nYang2025\n========\n\nMulti-day MI-BCI dataset (WBCIC-SHU) from Yang et al 2025.\n\nDataset 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\nAcquisition\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\nParticipants\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\nExperimental 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\nHED Event Annotations\n---------------------\n  Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\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\nParadigm-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\nData 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\nSignal 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\nCross-Validation\n----------------\n  Method: 10-fold\n  Folds: 10\n  Evaluation type: within_session\n\nBCI Application\n---------------\n  Applications: motor_control\n  Environment: laboratory\n  Online feedback: False\n\nTags\n----\n  Pathology: Healthy\n  Modality: Motor\n  Type: Research\n\nDocumentation\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\nReferences\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":6,"publish_date":null,"embedding_dirty":0,"license_tier":"attribution","zarr_status":"ready","zarr_converted_at":"2026-09-05 04:54:39","zarr_store_count":153,"zarr_index_etag":"98482afd6a0eecc226226feb1bb2dc74","zarr_source_commit":"3e75f4a92c8912c7861090c1bffb041d1cccf00f","archive_status":null,"archive_size":49887711785,"archive_retry_count":0,"records_status":"ready","archive_skip_reason":"dataset 153.8 GB exceeds 100.0 GB archive limit; use direct download","zarr_errors":0,"zarr_failure_count":0,"zarr_deterministic":0,"zarr_failed_at":null,"num_dataset_citations":0,"num_datapaper_citations":34,"n_channels":59,"electrode_system":"10-10","has_hed":1,"hed_version":"8.4.0","is_exemplar":0,"bytes_present":165137554615,"data_complete":1,"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":64,"channel_count_max":64,"sampling_frequency":1000,"power_line_frequency":50,"eeg_reference":null,"placement_scheme":"10-05 system","sweep_stamps":"{\"enrichment_updated_at\":\"2026-08-18 22:51:26\",\"metadata_updated_at\":\"2026-08-18 22:52:04\",\"archive_checked_at\":\"2026-08-18 22:52:20\",\"zarr_checked_at\":\"2026-06-07 17:58:40\",\"records_checked_at\":\"2026-08-18 22:52:58\",\"citations_updated_at\":\"2026-09-08 03:00:48\",\"channel_montage_checked_at\":\"2026-06-28 23:05:32\",\"hed_checked_at\":\"2026-06-30 04:35:54\",\"data_checked_at\":null,\"availability_report_at\":\"2026-07-23 01:11:18\",\"signal_defaults_at\":\"2026-09-02 11:53:58\",\"recording_stats_at\":\"2026-09-06 03:01:01\"}","participants":51,"num_citations":34,"latest_version":"v1.0.5","zarr_verify_status":null,"zarr_verified_at":null,"owner_username":"bruaristimunha","owner_github":"bruAristimunha","file_size_formatted":"154 GB","zarr_data_failures":null,"zarr_index_url":"https://zarr.nemar.org/nm000348/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}}