{"success":true,"database":"eegdash","data":{"_id":"69d16e06897a7725c66f4ce5","dataset_id":"nm000347","associated_paper_doi":"10.1038/s41597-025-06100-7","authors":["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"],"bids_version":"1.9.0","contact_info":null,"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.1038/s41597-025-06100-7","datatypes":["eeg"],"demographics":{"subjects_count":37,"ages":[],"age_min":null,"age_max":null,"age_mean":null,"species":null,"sex_distribution":null,"handedness_distribution":{"r":37}},"experimental_modalities":null,"external_links":{"paper_url":"https://www.nature.com/articles/s41597-025-06100-7.pdf"},"funding":[],"ingestion_fingerprint":"f682f89697b8244160d6b5d7a980406c56dc9ff3b99ba51a1b517ee9c422c805","license":"CC-BY-NC-ND-4.0","n_contributing_labs":null,"name":"Shi et al. 2025 — HEFMI-ICH: a hybrid EEG-fNIRS motor imagery dataset for brain-computer interface in intracerebral hemorrhage","readme":"HefmiIch2025\n============\nHybrid EEG-fNIRS MI dataset for ICH from Shi et al 2025.\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\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: {}\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\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\nHED Event Annotations\n---------------------\n  Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n  left_hand\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Imagine\n          ├─ Move\n          └─ Left, Hand\n  right_hand\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Imagine\n          ├─ Move\n          └─ Right, Hand\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\nData Structure\n--------------\n  Trials: 3330\n  Trials context: 37 subjects x ~3 sessions x 30 trials = ~3330\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\nCross-Validation\n----------------\n  Method: 5-fold\n  Folds: 5\n  Evaluation type: within_subject\nBCI Application\n---------------\n  Applications: rehabilitation\n  Environment: clinical\n  Online feedback: False\nTags\n----\n  Pathology: Healthy, Stroke\n  Modality: Motor\n  Type: Clinical, Research\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\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\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---\nGenerated by MOABB 1.5.0 (Mother of All BCI Benchmarks)\nhttps://github.com/NeuroTechX/moabb","recording_modality":["eeg"],"senior_author":null,"sessions":["0","1","2","3","4","5"],"size_bytes":2766217706,"source":"nemar","storage":{"backend":"nemar","base":"s3://nemar/nm000347","raw_key":"dataset_description.json","dep_keys":["README","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["imagery"],"timestamps":{"digested_at":"2026-04-30T14:09:57.011731+00:00","dataset_created_at":null,"dataset_modified_at":"2026-04-11T21:09:17Z"},"total_files":98,"computed_title":"Shi et al. 2025 — HEFMI-ICH: a hybrid EEG-fNIRS motor imagery dataset for brain-computer interface in intracerebral hemorrhage","nchans_counts":[{"val":32,"count":98}],"sfreq_counts":[{"val":256.0,"count":98}],"stats_computed_at":"2026-05-01T13:49:34.659844+00:00","total_duration_s":112307.6171875,"tagger_meta":{"model":"gpt-6-sol","taxonomy":"v2","config_hash":"39931253008fe2df","tagged_at":"2026-10-06T13:13:11Z","source":"eegdash-llm-tagger"},"tags":{"pathology":["Stroke"],"modality":["Multisensory"],"type":["Motor","Clinical"],"confidence":{"pathology":0.9,"modality":0.8,"type":0.9},"reasoning":{"few_shot_analysis":"The EEG Motor Movement/Imagery example is the closest paradigm match: visually cued hand imagery maps to Motor, with modality determined by the cue rather than the imagined movement. The Parkinson's reinforcement-learning example shows that a patient-and-control dataset can receive both a task label and Clinical. Neither example supplies this dataset's population or stimulus facts.","metadata_analysis":"The readme states \"17 healthy, 20 ICH patients\" and \"Clinical population: intracerebral hemorrhage (ICH).\" It describes \"2-class hand MI (left/right grasping) for ICH rehabilitation\" and lists \"Stimulus type: directional arrow + auditory beep\" and \"Stimulus modalities: visual, auditory.\" The tags include \"Pathology: Healthy, Stroke,\" but the recruited patient condition, rather than the presence of controls, determines pathology.","paper_abstract_analysis":"The abstract confirms \"20 patients with ICH\" performing \"left-right hand motor imagery (MI) paradigms.\" Its stated aims—MI decoding algorithms and ICH rehabilitation systems—support Motor and Clinical. It does not establish that a therapy was administered, so Intervention is not selected.","evidence_alignment_check":"Pathology: Metadata says \"Clinical population: intracerebral hemorrhage (ICH)\"; the patient-and-control few-shot pattern suggests labeling the patients' condition. ALIGN: select Stroke. Modality: Metadata says \"directional arrow + auditory beep\" and \"Stimulus modalities: visual, auditory\"; the imagery few-shot suggests using the cue's sensory channel. ALIGN on the rule, though this dataset has two channels rather than that example's one: select Multisensory despite \"Primary modality: visual.\" Type: Metadata says \"2-class hand MI\" and \"for ICH rehabilitation\"; imagery and clinical-cohort examples suggest Motor plus Clinical. ALIGN. No metadata fact is overridden by a few-shot pattern.","decision_summary":"Pathology—Stroke versus Healthy: \"20 ICH patients,\" \"Clinical population: intracerebral hemorrhage (ICH),\" and \"Pathology: Healthy, Stroke\" favor Stroke; controls do not change the recruited clinical condition (0.9). Modality—Multisensory versus Visual: \"directional arrow + auditory beep\" and \"Stimulus modalities: visual, auditory\" favor Multisensory; \"Primary modality: visual\" supports the runner-up but does not remove the auditory cue (0.8). Type—Motor plus Clinical versus Motor alone: \"2-class hand MI (left/right grasping),\" \"for ICH rehabilitation,\" and the abstract's MI-decoding and rehabilitation aims support both labels (0.9). Few-shot conventions and metadata align on all selections."}},"canonical_name":null,"name_confidence":0.72,"name_meta":{"suggested_at":"2026-04-14T10:18:35.344Z","model":"openai/gpt-5.2 + openai/gpt-5.4-mini + deterministic_fallback"},"name_source":"canonical","author_year":"HefmiIch2025","associated_paper_meta":{"channel":"text/readme","confidence":"high","author_overlap":8,"is_oa":true,"oa_status":"gold","source":"paper_resolver"}}}