{"dataset":{"id":"182","dataset_id":"nm000149","name":"BNCI 2019-001 Motor Imagery dataset for Spinal Cord Injury patients","description":"This dataset comprises EEG recordings from 10 patients with spinal cord injury performing five distinct hand and forearm motor imagery tasks: hand open, palmar grasp, lateral grasp, pronation, and supination. The study investigates the decoding of attempted movements from low-frequency EEG signals (movement-related cortical potentials) to support neuroprosthetic applications for upper limb control. Data include 360 trials (72 per class) acquired at 256 Hz with 61 EEG channels and 3 EOG channels, preprocessed with ICA-based artifact rejection and bandpass filtering.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000149","concept_doi":"10.82901/nemar.nm000149","latest_version_doi":"10.82901/nemar.nm000149.v1.0.2","created_at":"2026-03-22 18:59:02","updated_at":"2026-08-18 18:15:37","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"BNCI 2019-001 Motor Imagery dataset for Spinal Cord Injury patients\",\n  \"description\": \"This dataset comprises EEG recordings from 10 patients with spinal cord injury performing five distinct hand and forearm motor imagery tasks: hand open, palmar grasp, lateral grasp, pronation, and supination. The study investigates the decoding of attempted movements from low-frequency EEG signals (movement-related cortical potentials) to support neuroprosthetic applications for upper limb control. Data include 360 trials (72 per class) acquired at 256 Hz with 61 EEG channels and 3 EOG channels, preprocessed with ICA-based artifact rejection and bandpass filtering.\",\n  \"methods_description\": \"EEG data were acquired at 256 Hz using a 61-channel g.tec system with active electrodes in a 10-5 montage, referenced to the left earlobe with ground at AFF2h. Participants performed five motor imagery tasks cued visually for 3 seconds followed by 3 seconds of imagery execution. Preprocessing included 50 Hz notch filtering, 0.01-100 Hz bandpass filtering, ICA-based artifact removal, and trial rejection for amplitude exceeding ±100 μV or abnormal statistical properties. Final analysis used 0.3-3 Hz bandpass filtering with Common Average Reference spatial filtering.\",\n  \"license\": \"CC-BY-4.0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"Patrick Ofner\": {\n      \"orcid\": \"0000-0001-7169-4300\"\n    },\n    \"Andreas Schwarz\": {\n      \"orcid\": \"0000-0002-3883-4989\"\n    },\n    \"Joana Pereira\": {\n      \"orcid\": \"0000-0002-2032-8981\"\n    },\n    \"Daniela Wyss\": {},\n    \"Renate Wildburger\": {},\n    \"Gernot R. Müller-Putz\": {\n      \"orcid\": \"0000-0002-0087-3720\"\n    }\n  },\n  \"keywords\": [\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"motor imagery\"\n    },\n    {\n      \"term\": \"spinal cord injury\"\n    },\n    {\n      \"term\": \"brain-computer interfaces\"\n    },\n    {\n      \"term\": \"movement-related cortical potentials\"\n    },\n    {\n      \"term\": \"neuroprosthetics\"\n    },\n    {\n      \"term\": \"hand grasp control\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.1038/s41598-019-43594-9\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"10.5281/zenodo.2222268\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/nm000149\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/nm000149\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"European Commission\",\n      \"award_number\": \"H2020-643955\",\n      \"award_title\": \"MoreGrasp\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"EEG Dataset\",\n  \"modalities\": [\n    \"eeg\"\n  ],\n  \"sizes\": [\n    \"4.8 GB (183 files)\"\n  ],\n  \"formats\": [\n    \".bdf\",\n    \".gdf\",\n    \".html\",\n    \".json\",\n    \".md\",\n    \".tsv\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"source_hash\": \"28833d65d996ec42138b4a5b62f2dcbd480982373e635af2f2ec4897f08b1b21\"\n}","last_activity_at":"2026-08-16 13:27:25","source":null,"source_id":null,"subject_count":10,"modalities":"eeg","age_min":2018,"age_max":2018,"file_size":4847154462,"total_files":693,"tasks":"imagery","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Patrick Ofner, Andreas Schwarz, Joana Pereira, Daniela Wyss, Renate Wildburger, Gernot R. Müller-Putz","license":"CC-BY-4.0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000149-blue)](https://doi.org/10.82901/nemar.nm000149)\n\n# BNCI 2019-001 Motor Imagery dataset for Spinal Cord Injury patients\n\nBNCI 2019-001 Motor Imagery dataset for Spinal Cord Injury patients.\n\n## Dataset Overview\n\n- **Code**: BNCI2019-001\n- **Paradigm**: imagery\n- **DOI**: 10.1038/s41598-019-43594-9\n- **Subjects**: 10\n- **Sessions per subject**: 1\n- **Events**: supination=776, pronation=777, hand_open=779, palmar_grasp=925, lateral_grasp=926\n- **Trial interval**: [2, 5] s\n- **Runs per session**: 9\n- **File format**: GDF\n- **Contributing labs**: Graz University of Technology Institute of Neural Engineering BCI-Lab, AUVA rehabilitation clinic Tobelbad\n\n## Acquisition\n\n- **Sampling rate**: 256.0 Hz\n- **Number of channels**: 61\n- **Channel types**: eeg=61, eog=3\n- **Channel names**: AFz, C1, C2, C3, C4, C5, C6, CCP1h, CCP2h, CCP3h, CCP4h, CCP5h, CCP6h, CP1, CP2, CP3, CP4, CP5, CP6, CPP1h, CPP2h, CPP3h, CPP4h, CPP5h, CPP6h, CPz, Cz, F1, F2, F3, F4, FC1, FC2, FC3, FC4, FC5, FC6, FCC1h, FCC2h, FCC3h, FCC4h, FCC5h, FCC6h, FCz, FFC1h, FFC2h, FFC3h, FFC4h, FFC5h, FFC6h, Fz, P1, P2, P3, P4, P5, P6, POz, PPO1h, PPO2h, Pz, eog-l, eog-m, eog-r\n- **Montage**: 10-5\n- **Hardware**: g.tec\n- **Software**: EEGlab 14.1.1b\n- **Reference**: left earlobe\n- **Ground**: AFF2h\n- **Sensor type**: active electrode\n- **Line frequency**: 50.0 Hz\n- **Online filters**: 50 Hz notch, 0.01-100 Hz bandpass\n- **Cap manufacturer**: g.tec medical engineering GmbH\n- **Cap model**: g.GAMMAsys/g.LADYbird\n- **Electrode type**: active electrode\n- **Auxiliary channels**: EOG (3 ch, above nasion, below outer canthi left, below outer canthi right)\n\n## Participants\n\n- **Number of subjects**: 10\n- **Health status**: patients\n- **Clinical population**: spinal cord injury\n- **Age**: mean=49.8, min=20, max=78\n- **Gender distribution**: male=9, female=1\n- **Handedness**: right-handed (all participants originally)\n- **Species**: human\n\n## Experimental Protocol\n\n- **Paradigm**: imagery\n- **Task type**: attempted movement\n- **Number of classes**: 5\n- **Class labels**: supination, pronation, hand_open, palmar_grasp, lateral_grasp\n- **Trial duration**: 5.0 s\n- **Tasks**: hand_open, palmar_grasp, lateral_grasp, pronation, supination\n- **Study design**: motor imagery and attempted movements\n- **Feedback type**: visual feedback (online paradigm only - movement icon displayed when movement detected)\n- **Stimulus type**: visual cue\n- **Stimulus modalities**: visual, auditory\n- **Primary modality**: visual\n- **Synchronicity**: synchronous\n- **Mode**: both\n- **Training/test split**: True\n- **Instructions**: Participants were instructed to attempt or execute movements based on class cue displayed on screen. They were asked to focus gaze on fixation cross, avoid eye movements, swallowing, and blinking during trial period.\n\n## HED Event Annotations\n\nSchema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n\n```\n  supination\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Imagine\n          ├─ Turn\n          ├─ Forearm\n          └─ Label/supination\n\n  pronation\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Imagine\n          ├─ Turn\n          ├─ Forearm\n          └─ Label/pronation\n\n  hand_open\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Imagine, Open, Hand\n\n  palmar_grasp\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Imagine, Grasp, Hand\n\n  lateral_grasp\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Imagine\n          ├─ Grasp\n          ├─ Hand\n          └─ Label/lateral\n\n```\n## Paradigm-Specific Parameters\n\n- **Detected paradigm**: motor_imagery\n- **Imagery tasks**: hand_open, palmar_grasp, lateral_grasp, pronation, supination\n- **Cue duration**: 3.0 s\n- **Imagery duration**: 3.0 s\n\n## Data Structure\n\n- **Trials**: 360\n- **Trials per class**: hand_open=72, palmar_grasp=72, lateral_grasp=72, pronation=72, supination=72\n- **Blocks per session**: 9\n- **Trials context**: total per subject (72 trials per class)\n\n## Preprocessing\n\n- **Data state**: raw (GDF format)\n- **Preprocessing applied**: True\n- **Steps**: bandpass filter, notch filter, ICA, artifact rejection\n- **Highpass filter**: 0.01 Hz\n- **Lowpass filter**: 100 Hz\n- **Bandpass filter**: {'low_cutoff_hz': 0.01, 'high_cutoff_hz': 100.0}\n- **Notch filter**: [50] Hz\n- **Filter type**: Chebyshev\n- **Filter order**: 8\n- **Artifact methods**: ICA, visual inspection, abnormal joint probability, abnormal kurtosis\n- **Re-reference**: CAR\n- **Notes**: Noisy channels were visually inspected and removed. AFz was removed by default as it is sensitive to eye blinks and eye movements. ICA was performed on 0.3-70 Hz filtered signals using extended infomax. PCA dimensionality reduction retained 99% variance. Artifact-contaminated ICs (muscle and eye-related) were removed. Trials with values above/below ±100 μV, abnormal joint probabilities, or abnormal kurtosis (5x SD threshold) were rejected. Final analysis used 0.3-3 Hz bandpass filter.\n\n## Signal Processing\n\n- **Classifiers**: Shrinkage LDA, sLDA\n- **Feature extraction**: time-domain low-frequency signals, MRCPs, ICA\n- **Frequency bands**: analyzed=[0.3, 3.0] Hz\n- **Spatial filters**: CAR\n\n## Cross-Validation\n\n- **Method**: 10x10-fold\n- **Folds**: 10\n- **Evaluation type**: within_subject, cross_validation\n\n## Performance (Original Study)\n\n- **Accuracy**: 45.3%\n- **Peak Accuracy 5Class**: 45.3\n- **Peak Latency 5Class S**: 1.1\n- **Confidence Interval Lower**: 40.3\n- **Confidence Interval Upper**: 50.3\n- **Chance Level 5Class**: 20.0\n- **Significance Level 5Class**: 22.3\n- **Peak Accuracy 3Class Subset**: 53.0\n- **Peak Latency 3Class Subset S**: 1.0\n- **Online Accuracy 2Class**: 68.4\n- **Online Tpr**: 31.75\n- **Online Fp Per Min**: 3.4\n\n## BCI Application\n\n- **Applications**: neuroprosthetic, upper_limb_control, hand_grasp_control\n- **Environment**: indoor\n- **Online feedback**: True\n\n## Tags\n\n- **Pathology**: Spinal Cord Injury\n- **Modality**: Motor\n- **Type**: Motor\n\n## Documentation\n\n- **Description**: This dataset investigates whether attempted arm and hand movements in persons with spinal cord injury can be decoded from low-frequency EEG signals (MRCPs). The study includes offline 5-class classification and online proof-of-concept for self-paced movement detection.\n- **DOI**: 10.1038/s41598-019-43594-9\n- **Associated paper DOI**: 10.1038/s41598-019-43594-9\n- **License**: CC-BY-4.0\n- **Investigators**: Patrick Ofner, Andreas Schwarz, Joana Pereira, Daniela Wyss, Renate Wildburger, Gernot R. Müller-Putz\n- **Senior author**: Gernot R. Müller-Putz\n- **Contact**: gernot.mueller@tugraz.at\n- **Institution**: Graz University of Technology\n- **Department**: Institute of Neural Engineering, BCI-Lab\n- **Address**: Graz, Austria\n- **Country**: Austria\n- **Repository**: Zenodo\n- **Data URL**: https://doi.org/10.5281/zenodo.2222268\n- **Publication year**: 2019\n- **Funding**: European ICT Programme Project H2020-643955 'MoreGrasp'\n- **Ethics approval**: Ethics committee for the hospitals of the Austrian general accident insurance institution AUVA (approval number 3/2017)\n- **Acknowledgements**: This work is supported by the European ICT Programme Project H2020-643955 'MoreGrasp'.\n\n## Abstract\n\nWe show that persons with spinal cord injury (SCI) retain decodable neural correlates of attempted arm and hand movements. We investigated hand open, palmar grasp, lateral grasp, pronation, and supination in 10 persons with cervical SCI. Discriminative movement information was provided by the time-domain of low-frequency electroencephalography (EEG) signals. Based on these signals, we obtained a maximum average classification accuracy of 45% (chance level was 20%) with respect to the five investigated classes. Pattern analysis indicates central motor areas as the origin of the discriminative signals. 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