{"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":"e40722947d662781d343b75a38fa4c429865524453287edc32518fbc05aaa6db","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":60831759390,"source":"nemar","storage":{"backend":"nemar","base":"s3://nemar/nm000270","raw_key":"dataset_description.json","dep_keys":["README.md"]},"study_design":null,"study_domain":null,"tasks":["MotorImagery","imagery"],"timestamps":{"digested_at":"2026-08-26T11:04:28.678073+00:00","dataset_created_at":null,"dataset_modified_at":"2026-08-20 19:22:03"},"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":529}],"sfreq_counts":[{"val":1000.0,"count":529}],"stats_computed_at":"2026-08-27T07:51:42.858482+00:00","total_duration_s":152804.47100000002,"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"11b8c36365c954ec","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Unknown"],"modality":["Unknown"],"type":["Motor"],"confidence":{"pathology":0.5,"modality":0.5,"type":0.7},"reasoning":{"few_shot_analysis":"Most similar few-shot example by paradigm is the \"EEG Motor Movement/Imagery Dataset\" (Schalk et al.), which contains motor imagery runs and is labeled Type=Motor (with Modality=Visual due to on-screen cues). This guides mapping the task name \"imagery\" to Type=Motor when imagery is the explicit paradigm name. However, unlike the example, this dataset’s metadata does not describe the cue/stimulus channel (visual/auditory/etc.), so Modality cannot be confidently inferred from few-shot conventions alone.","metadata_analysis":"Key available metadata is extremely sparse. Quotes:\n1) Title: \"liu2025 - NEMAR Dataset\".\n2) Participants: \"Subjects: 27\".\n3) Task list includes: \"imagery\" (alongside non-task entries like \".gitignore\" and \"Liu2025.metadata\").\nNo metadata text specifies a clinical population, diagnosis, stimulus type, or concrete task description beyond the name \"imagery\".","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology:\n- Metadata says: only \"Subjects: 27\"; no diagnosis/condition stated.\n- Few-shot pattern suggests: many imagery datasets are Healthy, but that is not a stated fact here.\n- Alignment: cannot assess; no explicit pathology facts. Therefore label should not be forced to Healthy.\n\nModality:\n- Metadata says: task name \"imagery\" only; no stimulus/cue modality described.\n- Few-shot pattern suggests: motor imagery tasks are often visually cued (thus sometimes labeled Visual), but this is not explicitly stated for this dataset.\n- Alignment: conflicts in certainty (few-shot suggests likely Visual; metadata provides no confirming facts). Metadata insufficiency means we avoid over-committing.\n\nType:\n- Metadata says: task includes \"imagery\".\n- Few-shot pattern suggests: imagery paradigms map to Type=Motor (as in the motor movement/imagery example).\n- Alignment: aligns reasonably (task name directly indicates imagery; Motor is the closest construct label).","decision_summary":"Top-2 candidates and selection:\n\n1) Pathology:\n- Candidate A: Unknown — supported by absence of any clinical recruitment info (only \"Subjects: 27\").\n- Candidate B: Healthy — plausible by convention for many non-clinical imagery datasets, but not stated.\nDecision: Unknown (metadata does not state Healthy or any disorder). Evidence alignment: insufficient metadata; do not infer pathology.\n\n2) Modality (stimulus/input channel):\n- Candidate A: Unknown — supported by no mention of visual/auditory/tactile cues; only \"imagery\" is provided.\n- Candidate B: Visual — plausible because motor imagery tasks are commonly visually cued (few-shot motor imagery example labeled Visual), but not confirmed here.\nDecision: Unknown (cannot verify cue modality from metadata). Evidence alignment: few-shot suggests Visual but conflicts with lack of explicit metadata.\n\n3) Type (construct/purpose):\n- Candidate A: Motor — supported by task name \"imagery\" and few-shot convention mapping imagery paradigms to Motor.\n- Candidate B: Other — if \"imagery\" referred to non-motor mental imagery (e.g., visual imagery), but no supporting description.\nDecision: Motor. Evidence alignment: metadata term + few-shot convention are consistent.\n\nConfidence justification:\n- Pathology confidence limited (no quotes indicating any pathology or health status).\n- Modality confidence low (no quotes about stimulus channel).\n- Type confidence moderate (one explicit cue: task name \"imagery\"; plus strong few-shot analog)."}},"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}}