{"dataset":{"id":"175","dataset_id":"nm000142","name":"Ear-EEG motor execution dataset from Wu et al 2020","description":"This dataset contains simultaneous scalp and ear-EEG recordings from 6 healthy right-handed adults performing a motor execution task involving left and right hand fist clenching, cued by visual arrow stimuli. Recordings were made at 1000 Hz using a 122-channel Neuroscan SynAmps2 system, yielding 1114 trials, and are intended for benchmarking motor task classification algorithms such as EEGNet. The dataset was converted to BIDS format using MOABB and originates from a study investigating the utility of in-ear EEG sensing for motor task classification.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000142","concept_doi":"10.82901/nemar.nm000142","latest_version_doi":"10.82901/nemar.nm000142.v1.0.3","created_at":"2026-03-22 16:16:45","updated_at":"2026-08-20 19:21:20","zenodo_concept_id":"19632286","is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"Ear-EEG motor execution dataset from Wu et al 2020\",\n  \"description\": \"This dataset contains simultaneous scalp and ear-EEG recordings from 6 healthy right-handed adults performing a motor execution task involving left and right hand fist clenching, cued by visual arrow stimuli. Recordings were made at 1000 Hz using a 122-channel Neuroscan SynAmps2 system, yielding 1114 trials, and are intended for benchmarking motor task classification algorithms such as EEGNet. The dataset was converted to BIDS format using MOABB and originates from a study investigating the utility of in-ear EEG sensing for motor task classification.\",\n  \"methods_description\": \"EEG was recorded at 1000 Hz using a 122-channel Neuroscan SynAmps2 system with Ag/AgCl electrodes arranged per the standard_1005 montage, with online bandpass filtering (0.5–100 Hz) and 50 Hz line frequency. Subjects performed a synchronous motor execution task (left/right hand fist clenching) cued by visual arrow stimuli, with 4-second trial durations across multiple sessions.\",\n  \"license\": \"CC-BY-4.0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"Xiaoli Wu\": {\n      \"orcid\": \"0000-0002-5651-1673\"\n    },\n    \"Wenhui Zhang\": {\n      \"orcid\": \"0000-0002-7672-3104\"\n    },\n    \"Zhibo Fu\": {\n      \"orcid\": \"0000-0003-0099-3201\"\n    },\n    \"Roy T.H. Cheung\": {},\n    \"Rosa H.M. 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Chan","license":"CC-BY-4.0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000142-blue)](https://doi.org/10.82901/nemar.nm000142)\n\n# Ear-EEG motor execution dataset from Wu et al 2020\n\nEar-EEG motor execution dataset from Wu et al 2020.\n\n## Dataset Overview\n\n- **Code**: Wu2020\n- **Paradigm**: imagery\n- **DOI**: 10.1088/1741-2552/abc1b6\n- **Subjects**: 6\n- **Sessions per subject**: 1\n- **Events**: left_hand=1, right_hand=2\n- **Trial interval**: [0, 4] s\n- **File format**: Curry\n\n## Acquisition\n\n- **Sampling rate**: 1000.0 Hz\n- **Number of channels**: 122\n- **Channel types**: eeg=122, misc=10\n- **Montage**: standard_1005\n- **Hardware**: Neuroscan SynAmps2\n- **Reference**: scalp REF\n- **Ground**: scalp GRD\n- **Sensor type**: Ag/AgCl\n- **Line frequency**: 50.0 Hz\n- **Online filters**: {'bandpass': [0.5, 100]}\n\n## Participants\n\n- **Number of subjects**: 6\n- **Health status**: healthy\n- **Age**: mean=25.0, min=22.0, max=28.0\n- **Gender distribution**: female=4, male=2\n- **Handedness**: right-handed\n- **Species**: human\n\n## Experimental Protocol\n\n- **Paradigm**: imagery\n- **Number of classes**: 2\n- **Class labels**: left_hand, right_hand\n- **Trial duration**: 4.0 s\n- **Study design**: Motor execution (fist clenching) with simultaneous scalp and ear-EEG recording\n- **Feedback type**: none\n- **Stimulus type**: arrow cues\n- **Stimulus modalities**: visual, auditory\n- **Primary modality**: visual\n- **Synchronicity**: synchronous\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_hand\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Imagine\n          ├─ Move\n          └─ Left, Hand\n\n  right_hand\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Imagine\n          ├─ Move\n          └─ Right, Hand\n\n```\n## Paradigm-Specific Parameters\n\n- **Detected paradigm**: motor_imagery\n- **Imagery tasks**: left_hand, right_hand\n\n## Data Structure\n\n- **Trials**: 1114\n- **Trials context**: S1: 240, S2: 160, S3: 160, S4: 80, S5: 234, S6: 240 = 1114\n\n## Signal Processing\n\n- **Classifiers**: EEGNet\n\n## Cross-Validation\n\n- **Evaluation type**: within_subject\n\n## BCI Application\n\n- **Applications**: motor_control\n- **Environment**: laboratory\n- **Online feedback**: False\n\n## Tags\n\n- **Pathology**: Healthy\n- **Modality**: Motor\n- **Type**: Research\n\n## Documentation\n\n- **DOI**: 10.1088/1741-2552/abc1b6\n- **License**: CC-BY-4.0\n- **Investigators**: Xiaoli Wu, Wenhui Zhang, Zhibo Fu, Roy T.H. Cheung, Rosa H.M. Chan\n- **Institution**: City University of Hong Kong\n- **Country**: HK\n- **Repository**: Zenodo\n- **Data URL**: https://zenodo.org/records/18961128\n- **Publication year**: 2020\n\n## References\n\nWu, X., Zhang, W., Fu, Z., Cheung, R. T. H., & Chan, R. H. M. (2020). An investigation of in-ear sensing for motor task classification. Journal of Neural Engineering, 17(6), 066029. https://doi.org/10.1088/1741-2552/abc1b6\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). 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