{"success":true,"database":"eegdash","data":{"_id":"69d16e05897a7725c66f4ccd","dataset_id":"nm000270","associated_paper_doi":null,"authors":["Yuan Liu","Zhuolan Gui","De Yan","Zhuang Wang","Ruisi Gao","Ningxin Han","Junying Chen","Jialing Wu","Dong Ming"],"bids_version":"1.9.0","contact_info":null,"contributing_labs":null,"data_processed":false,"dataset_doi":"10.82901/nemar.nm000270","datatypes":["eeg"],"demographics":{"subjects_count":36,"ages":[],"age_min":null,"age_max":null,"age_mean":null,"species":null,"sex_distribution":null,"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://nemar.org/dataexplorer/detail/nm000270","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"8aa925127efbdb5745aa79471dc46972fbd40a8132f7a7d24ac583acce2fa7f1","license":"CC-BY-NC-ND-4.0","n_contributing_labs":null,"name":"Liu et al. 2025 — Lower limb motor imagery EEG dataset based on the multi-paradigm and longitudinal-training of stroke patients (Tianjin University)","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000270-blue)](https://doi.org/10.82901/nemar.nm000270)\n# Lower limb motor imagery EEG dataset of stroke patients (multi-paradigm, longitudinal training)\n## Summary\nThis dataset contains scalp electroencephalography (EEG) recordings acquired during a\n**lower-limb motor-imagery** paradigm in **stroke patients** undergoing rehabilitation.\nIt was collected with a multi-paradigm protocol and a longitudinal (repeated-session)\ntraining design, supporting research on motor-imagery brain–computer interfaces (BCI)\nfor lower-limb and gait rehabilitation after stroke.\nThe BIDS conversion in this NEMAR record contains EEG data for **27 participants**\n(`sub-*` folders), organised under the `eeg/` modality with a single motor-imagery\ntask (`task-imagery`).\n## Modality and paradigm\n- **Modality:** EEG (scalp electroencephalography)\n- **Task / paradigm:** Lower-limb motor imagery (`task-imagery`), multi-paradigm,\n  longitudinal training\n- **Population:** Stroke patients (rehabilitation cohort)\n## Participants\nThis BIDS dataset includes **27 subjects** (`sub-01` … ). Refer to `participants.tsv`\nand the original data descriptor for demographic and clinical details.\n## Original dataset / data paper\nPlease cite the original Scientific Data descriptor when using this dataset:\n> Liu, Y., Gui, Z., Yan, D., Wang, Z., Gao, R., Han, N., Chen, J., Wu, J., & Ming, D.\n> (2025). *Lower limb motor imagery EEG dataset based on the multi-paradigm and\n> longitudinal-training of stroke patients.* **Scientific Data, 12, 314.**\n> https://doi.org/10.1038/s41597-025-04618-4\n- **DOI:** [10.1038/s41597-025-04618-4](https://doi.org/10.1038/s41597-025-04618-4)\n- **Source / project:** Tianjin University\n- **Related record:** https://zenodo.org/records/18987384\n## Attribution\nAll data were collected by the original authors (Yuan Liu and colleagues, Tianjin\nUniversity). Please credit the original creators and cite the data paper above. This\nNEMAR record redistributes the dataset in BIDS format; EEG-BIDS and related BIDS tools\nwere used only for standardisation, not as the source of the data.\n## License\nCC-BY-NC-ND-4.0 (see `dataset_description.json`).","recording_modality":["eeg"],"senior_author":"Dong Ming","sessions":["0pre","follow","miies","mises","post","pre"],"size_bytes":60831824430,"source":"nemar","storage":{"backend":"nemar","base":"s3://nemar/nm000270","raw_key":"dataset_description.json","dep_keys":["README.md","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["MotorImagery","imagery"],"timestamps":{"digested_at":"2026-10-22T08:23:43.699496+00:00","dataset_created_at":null,"dataset_modified_at":"2026-10-20 18:08:54"},"total_files":529,"computed_title":"Liu et al. 2025 — Lower limb motor imagery EEG dataset based on the multi-paradigm and longitudinal-training of stroke patients (Tianjin University)","nchans_counts":[{"val":64,"count":110}],"sfreq_counts":[{"val":1000.0,"count":110}],"stats_computed_at":"2026-10-22T09:54:07.649949+00:00","total_duration_s":32392.0,"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":["Unknown"],"type":["Motor","Clinical"],"confidence":{"pathology":0.9,"modality":0.35,"type":0.8},"reasoning":{"few_shot_analysis":"The EEG Motor Movement/Imagery example maps motor imagery to Type Motor; its Modality Visual depends on an explicitly described screen target. The Parkinson's reinforcement-learning and TBI task examples show that a task in a recruited clinical cohort can receive both a task-focused Type and Clinical. These are labeling conventions, not evidence that this dataset used visual cues.","metadata_analysis":"The title identifies a \"lower limb motor imagery EEG dataset\" of \"stroke patients.\" The README confirms \"stroke patients undergoing rehabilitation,\" a \"lower-limb motor-imagery\" paradigm, and research on \"motor-imagery brain–computer interfaces (BCI)\" for rehabilitation. It also describes \"longitudinal (repeated-session) training\" but does not state whether imagery was cued or describe any sensory stimulus. The overview says \"Subjects: 36,\" whereas the README says \"27 participants\"; that count discrepancy does not affect the labels.","paper_abstract_analysis":"No paper abstract was provided. The README cites a data descriptor but supplies no further stimulus details.","evidence_alignment_check":"Pathology: Metadata says \"stroke patients\" and \"Population: Stroke patients (rehabilitation cohort).\" Few-shot clinical-cohort examples suggest labeling the recruited condition; they align, so Stroke wins. Modality: Metadata describes \"lower-limb motor-imagery\" but says nothing about cue delivery. The visual-cued motor-imagery example suggests Visual only when a screen target is documented; applying it here would conflict with the absence of a stated cue, so the dataset's missing stimulus information governs and Modality is Unknown. Type: Metadata says \"lower-limb motor-imagery\" and identifies a stroke rehabilitation cohort. The motor-imagery and clinical-cohort examples suggest Motor and Clinical; they align. The training design alone does not establish that an intervention's effects were studied.","decision_summary":"Pathology—Stroke versus Healthy: \"stroke patients,\" \"Population: Stroke patients (rehabilitation cohort),\" and the stroke-focused title directly support Stroke; there is no evidence for a healthy recruited cohort. Confidence 0.9, with three explicit metadata features and a clear clinical-cohort convention. Modality—Unknown versus Visual: No stimulus or cue channel is specified; Visual is plausible for cued imagery but unsupported here. Unknown wins, confidence 0.35 because neither channel nor absence of stimuli is established. Type—Motor and Clinical versus Intervention: \"lower-limb motor-imagery,\" \"motor-imagery brain–computer interfaces (BCI),\" and \"stroke patients undergoing rehabilitation\" support a motor-focused study in a clinical cohort. \"Longitudinal (repeated-session) training\" makes Intervention plausible, but an intervention-effect aim is not stated. Motor and Clinical win, confidence 0.8 based on multiple explicit features and matching few-shot conventions. No metadata fact was overridden by a demo pattern."}},"canonical_name":null,"name_confidence":0.56,"name_meta":{"suggested_at":"2026-04-14T10:18:35.344Z","model":"openai/gpt-5.2 + openai/gpt-5.4-mini + deterministic_fallback"},"name_source":"author_year","author_year":"Liu2025","associated_paper_meta":{"channel":"crossref-biblio","confidence":"high","author_overlap":8,"is_oa":true,"oa_status":"gold","source":"paper_resolver"},"acknowledgements":"Tianjin University","references_and_links":["https://doi.org/10.1038/s41597-025-04618-4","https://zenodo.org/records/18987384"],"source_datasets":[{"DOI":"doi:10.1038/s41597-025-04618-4"}],"bad_channels_info":null,"n_records":110}}