{"dataset":{"id":"263","dataset_id":"nm000230","name":"Lower-limb MI dataset for knee pain patients from Zuo et al. 2025","description":"A lower-limb motor imagery EEG dataset comprising 30 knee pain patients performing left and right leg flexion/extension imagery tasks across 5 sessions. The dataset contains 500 trials (250 per class) recorded at 500 Hz from 30 EEG channels using a standard 10-05 montage. This clinical dataset supports the development and evaluation of brain-computer interface classifiers for motor rehabilitation applications in patients with knee pain.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000230","concept_doi":"10.82901/nemar.nm000230","latest_version_doi":"10.82901/nemar.nm000230.v1.0.2","created_at":"2026-03-25 16:34:16","updated_at":"2026-08-18 21:18:32","zenodo_concept_id":"20519816","is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"Lower-limb MI dataset for knee pain patients from Zuo et al. 2025\",\n  \"description\": \"A lower-limb motor imagery EEG dataset comprising 30 knee pain patients performing left and right leg flexion/extension imagery tasks across 5 sessions. The dataset contains 500 trials (250 per class) recorded at 500 Hz from 30 EEG channels using a standard 10-05 montage. This clinical dataset supports the development and evaluation of brain-computer interface classifiers for motor rehabilitation applications in patients with knee pain.\",\n  \"methods_description\": \"This derivative dataset was generated from the original lower-limb motor imagery EEG data using MOABB (Mother of All BCI Benchmarks) version 1.5.0. EEG data were originally acquired from 30 knee pain patients (mean age 33.5 years, 12 female/18 male) using a ZhenTec EEG system with 30 channels at 500 Hz sampling rate. Participants performed cue-based motor imagery of left and right leg movements in 5 sessions, each containing 100 trials (50 left, 50 right leg imagery). Trial duration was 4 seconds with visual cue presentation. Reference electrode was CPz and ground was FPz. The derivative processing includes multiple feature extraction methods (CSP, FBCSP, deep learning, Riemannian geometry) and evaluation with 10-fold cross-validation for brain-computer interface classifier development.\",\n  \"license\": \"CC-BY-4.0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"Chongwen Zuo\": {\n      \"orcid\": \"0000-0003-4500-9685\"\n    },\n    \"Yi Yin\": {},\n    \"Haochong Wang\": {},\n    \"Zhiyang Zheng\": {},\n    \"Xiaoyan Ma\": {},\n    \"Yuan Yang\": {},\n    \"Jue Wang\": {},\n    \"Shan Wang\": {},\n    \"Zi-gang Huang\": {},\n    \"Chaoqun Ye\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"motor imagery\"\n    },\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"brain-computer interfaces\"\n    },\n    {\n      \"term\": \"knee pain\"\n    },\n    {\n      \"term\": \"rehabilitation\"\n    },\n    {\n      \"term\": \"lower limb\"\n    },\n    {\n      \"term\": \"clinical neurophysiology\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.1038/s41597-025-05767-2\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/nm000230\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/nm000230\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"EEG Dataset\",\n  \"modalities\": [\n    \"eeg\"\n  ],\n  \"sizes\": [\n    \"20.8 GB (237 files)\"\n  ],\n  \"formats\": [\n    \".bdf\",\n    \".json\",\n    \".mat\",\n    \".md\",\n    \".tsv\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"source_hash\": \"544236eca6b510ad2edf4a4f6449d7e78dbd0a58c1b5e016bb6dac11590ea691\"\n}","last_activity_at":"2026-08-16 13:34:36","source":null,"source_id":null,"subject_count":30,"modalities":"eeg","age_min":33.5,"age_max":33.5,"file_size":20814986500,"total_files":1427,"tasks":"MI,imagery","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Chongwen Zuo, Yi Yin, Haochong Wang, Zhiyang Zheng, Xiaoyan Ma, Yuan Yang, Jue Wang, Shan Wang, Zi-gang Huang, Chaoqun Ye","license":"CC-BY-4.0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000230-blue)](https://doi.org/10.82901/nemar.nm000230)\n\n# Lower-limb MI dataset for knee pain patients from Zuo et al. 2025\n\nLower-limb MI dataset for knee pain patients from Zuo et al. 2025.\n\n## Dataset Overview\n\n- **Code**: Zuo2025\n- **Paradigm**: imagery\n- **DOI**: 10.1038/s41597-025-05767-2\n- **Subjects**: 30\n- **Sessions per subject**: 5\n- **Events**: left_leg=1, right_leg=2\n- **Trial interval**: [0, 4] s\n- **File format**: MAT\n\n## Acquisition\n\n- **Sampling rate**: 500.0 Hz\n- **Number of channels**: 30\n- **Channel types**: eeg=30\n- **Channel names**: Fp1, Fp2, Fz, F3, F4, F7, F8, FCz, FC3, FC4, FT7, FT8, Cz, C3, C4, T3, T4, CPz, CP3, CP4, TP7, TP8, Pz, P3, P4, T5, T6, Oz, O1, O2\n- **Montage**: standard_1005\n- **Hardware**: ZhenTec EEG system\n- **Reference**: CPz\n- **Ground**: FPz\n- **Line frequency**: 50.0 Hz\n\n## Participants\n\n- **Number of subjects**: 30\n- **Health status**: knee pain patients\n- **Clinical population**: knee_pain\n- **Age**: mean=33.5, min=24, max=45\n- **Gender distribution**: female=12, male=18\n- **Species**: human\n\n## Experimental Protocol\n\n- **Paradigm**: imagery\n- **Number of classes**: 2\n- **Class labels**: left_leg, right_leg\n- **Trial duration**: 4.0 s\n- **Study design**: 2-class lower-limb MI (left/right leg flexion/extension). 5 sessions, 100 trials per session.\n- **Feedback type**: none\n- **Stimulus type**: visual\n- **Stimulus modalities**: visual\n- **Primary modality**: visual\n- **Synchronicity**: cue-based\n- **Mode**: offline\n\n## HED Event Annotations\n\nSchema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n\n```\n  left_leg\n    ├─ Sensory-event\n    └─ Label/left_leg\n\n  right_leg\n    ├─ Sensory-event\n    └─ Label/right_leg\n\n```\n## Paradigm-Specific Parameters\n\n- **Detected paradigm**: motor_imagery\n- **Imagery tasks**: left_leg, right_leg\n- **Imagery duration**: 4.0 s\n\n## Data Structure\n\n- **Trials**: 500\n- **Trials per class**: left_leg=250, right_leg=250\n- **Trials context**: 5 sessions x 100 trials (50 left + 50 right)\n\n## Signal Processing\n\n- **Classifiers**: CSP+LDA, FBCSP+SVM, EEGNet, OTFWRGD\n- **Feature extraction**: CSP, FBCSP, deep_learning, Riemannian_geometry\n- **Frequency bands**: alpha_mu=[8.0, 15.0] Hz; beta=[15.0, 30.0] Hz\n- **Spatial filters**: CSP, FBCSP\n\n## Cross-Validation\n\n- **Method**: 10-fold\n- **Folds**: 10\n- **Evaluation type**: within_subject\n\n## BCI Application\n\n- **Applications**: rehabilitation\n- **Environment**: clinical\n- **Online feedback**: False\n\n## Tags\n\n- **Pathology**: Knee Pain\n- **Modality**: Motor\n- **Type**: Clinical, Motor Imagery\n\n## Documentation\n\n- **DOI**: 10.1038/s41597-025-05767-2\n- **License**: CC-BY-4.0\n- **Investigators**: Chongwen Zuo, Yi Yin, Haochong Wang, Zhiyang Zheng, Xiaoyan Ma, Yuan Yang, Jue Wang, Shan Wang, Zi-gang Huang, Chaoqun Ye\n- **Institution**: Air Force Medical Center, Beijing\n- **Country**: CN\n- **Data URL**: https://figshare.com/articles/dataset/28740260\n- **Publication year**: 2025\n\n## References\n\nZuo, C., Yin, Y., Wang, H., et al. (2025). Enhancing classification of a large lower-limb motor imagery EEG dataset for BCI in knee pain patients. Scientific Data, 12, 1451. https://doi.org/10.1038/s41597-025-05767-2\n\nNotes\n\n.. versionadded:: 1.2.0\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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