{"dataset":{"id":"382","dataset_id":"nm000347","name":"Shi et al. 2025 — HEFMI-ICH: a hybrid EEG-fNIRS motor imagery dataset for brain-computer interface in intracerebral hemorrhage","description":"HEFMI-ICH is a hybrid EEG-fNIRS motor imagery dataset designed for brain-computer interface applications in intracerebral hemorrhage rehabilitation. The dataset comprises 37 participants (17 healthy controls and 20 ICH patients) performing 2-class hand motor imagery tasks (left and right hand grasping) across multiple sessions. With 32-channel EEG recordings at 256 Hz and 3,330 trials, this dataset supports the development and evaluation of BCI systems for clinical stroke rehabilitation.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000347","concept_doi":"10.82901/nemar.nm000347","latest_version_doi":"10.82901/nemar.nm000347.v1.0.3","created_at":"2026-03-28 17:57:21","updated_at":"2026-08-18 18:22:55","zenodo_concept_id":"20528390","is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"enriched\",\n  \"title\": \"Shi et al. 2025 — HEFMI-ICH: a hybrid EEG-fNIRS motor imagery dataset for brain-computer interface in intracerebral hemorrhage\",\n  \"description\": \"HEFMI-ICH is a hybrid EEG-fNIRS motor imagery dataset designed for brain-computer interface applications in intracerebral hemorrhage rehabilitation. The dataset comprises 37 participants (17 healthy controls and 20 ICH patients) performing 2-class hand motor imagery tasks (left and right hand grasping) across multiple sessions. With 32-channel EEG recordings at 256 Hz and 3,330 trials, this dataset supports the development and evaluation of BCI systems for clinical stroke rehabilitation.\",\n  \"methods_description\": \"EEG data were acquired using a g.HIamp amplifier (g.tec medical engineering GmbH) with 32 channels at 256 Hz sampling rate using a biosemi32 montage. Participants performed cued motor imagery tasks with 2-second visual cues (directional arrows) followed by 10-second imagery periods. The experimental protocol included 2-class imagery tasks (left/right hand grasping) with no feedback, conducted in an offline mode across 3 sessions per subject. Data were preprocessed with frequency band filtering (0.5-30 Hz) and spatially filtered using Common Spatial Patterns (CSP) and Filter Bank CSP (FBCSP) methods. fNIRS data were acquired concurrently with EEG to enable hybrid neuroimaging analysis.\",\n  \"license\": \"CC-BY-NC-ND-4.0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"Jian Shi\": {},\n    \"Danyang Chen\": {},\n    \"Xingwei Zhao\": {},\n    \"Zhixian Zhao\": {},\n    \"Shengjie Li\": {},\n    \"Yeguang Xu\": {},\n    \"Tao Ding\": {},\n    \"Zheng Zhu\": {},\n    \"Peng Zhang\": {},\n    \"Qing Ye\": {},\n    \"Yingxin Tang\": {},\n    \"Ping Zhang\": {},\n    \"Bo Tao\": {},\n    \"Zhouping Tang\": {\n      \"orcid\": \"0000-0002-4153-8590\"\n    }\n  },\n  \"keywords\": [\n    {\n      \"term\": \"EEG\"\n    },\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\": \"intracerebral hemorrhage\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D006442\"\n    },\n    {\n      \"term\": \"stroke rehabilitation\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D000071939\"\n    },\n    {\n      \"term\": \"stroke\"\n    },\n    {\n      \"term\": \"fNIRS\"\n    },\n    {\n      \"term\": \"hybrid neuroimaging\"\n    },\n    {\n      \"term\": \"neurorehabilitation\"\n    },\n    {\n      \"term\": \"rehabilitation\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.1038/s41597-025-06100-7\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/nm000347\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": 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Yeguang Xu, Tao Ding, Zheng Zhu, Peng Zhang, Qing Ye, Yingxin Tang, Ping Zhang, Bo Tao, Zhouping Tang","license":"CC-BY-NC-ND-4.0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000347-blue)](https://doi.org/10.82901/nemar.nm000347)\n\nHefmiIch2025\n============\n\nHybrid EEG-fNIRS MI dataset for ICH from Shi et al 2025.\n\nDataset Overview\n----------------\n  Code: HefmiIch2025\n  Paradigm: imagery\n  DOI: 10.1038/s41597-025-06100-7\n  Subjects: 37\n  Sessions per subject: 3\n  Events: left_hand=1, right_hand=2\n  Trial interval: [0, 10] s\n  File format: MAT (pre-epoched)\n  Data preprocessed: True\n\nAcquisition\n-----------\n  Sampling rate: 256.0 Hz\n  Number of channels: 32\n  Channel types: eeg=32\n  Channel names: FC1, AF3, AF4, CP1, CP2, CP6, Cz, C3, C4, T7, T8, FC2, FC5, FC6, Pz, CP5, PO3, PO4, Oz, Fp2, Fp1, Fz, F3, F4, F7, F8, P3, P4, P7, P8, O1, O2\n  Montage: biosemi32\n  Hardware: g.HIamp (g.tec medical engineering GmbH)\n  Line frequency: 50.0 Hz\n  Online filters: {}\n\nParticipants\n------------\n  Number of subjects: 37\n  Health status: mixed (17 healthy, 20 ICH patients)\n  Clinical population: intracerebral hemorrhage (ICH)\n  Age: min=20.0, max=65.0\n  Gender distribution: female=8, male=29\n  Handedness: right-handed\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: 27.0 s\n  Study design: 2-class hand MI (left/right grasping) for ICH rehabilitation. 17 healthy + 20 ICH patients, 1-6 sessions per subject.\n  Feedback type: none\n  Stimulus type: directional arrow + auditory beep\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\n  Cue duration: 2.0 s\n  Imagery duration: 10.0 s\n\nData Structure\n--------------\n  Trials: 3330\n  Trials context: 37 subjects x ~3 sessions x 30 trials = ~3330\n\nSignal Processing\n-----------------\n  Classifiers: CSP+SVM, FBCSP+SVM, EEGBaseNet, TF+SVM\n  Feature extraction: CSP, FBCSP, time-frequency features\n  Frequency bands: preprocessing=[0.5, 30.0] Hz\n  Spatial filters: CSP, FBCSP\n\nCross-Validation\n----------------\n  Method: 5-fold\n  Folds: 5\n  Evaluation type: within_subject\n\nBCI Application\n---------------\n  Applications: rehabilitation\n  Environment: clinical\n  Online feedback: False\n\nTags\n----\n  Pathology: Healthy, Stroke\n  Modality: Motor\n  Type: Clinical, Research\n\nDocumentation\n-------------\n  DOI: 10.1038/s41597-025-06100-7\n  License: CC-BY-NC-ND-4.0\n  Investigators: Jian Shi, Danyang Chen, Xingwei Zhao, Zhixian Zhao, Shengjie Li, Yeguang Xu, Tao Ding, Zheng Zhu, Peng Zhang, Qing Ye, Yingxin Tang, Ping Zhang, Bo Tao, Zhouping Tang\n  Institution: Huazhong University of Science and Technology\n  Country: CN\n  Data URL: https://figshare.com/articles/dataset/28955456\n  Publication year: 2025\n\nReferences\n----------\nShi, J., Chen, D., et al. (2025). HEFMI-ICH: a hybrid EEG-fNIRS motor imagery dataset for brain-computer interface in intracerebral hemorrhage. Scientific Data. https://doi.org/10.1038/s41597-025-06100-7\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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