{"dataset":{"id":"181","dataset_id":"nm000148","name":"Motor imagery BCI dataset with pupillometry augmentation","description":"A motor imagery brain-computer interface dataset comprising EEG recordings from 30 healthy participants performing left-hand grasping imagery and rest tasks, augmented with pupillometry measurements. The dataset includes 32-channel EEG data sampled at 512 Hz acquired using BioSemi ActiveTwo hardware, with two experimental runs per subject containing 50 trials total. This derivative dataset was processed using the Mother of All BCI Benchmarks (MOABB) framework and is designed to support BCI research and algorithm development.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000148","concept_doi":"10.82901/nemar.nm000148","latest_version_doi":"10.82901/nemar.nm000148.v1.0.2","created_at":"2026-03-22 18:58:38","updated_at":"2026-08-18 18:13:28","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"Motor imagery BCI dataset with pupillometry augmentation\",\n  \"description\": \"A motor imagery brain-computer interface dataset comprising EEG recordings from 30 healthy participants performing left-hand grasping imagery and rest tasks, augmented with pupillometry measurements. The dataset includes 32-channel EEG data sampled at 512 Hz acquired using BioSemi ActiveTwo hardware, with two experimental runs per subject containing 50 trials total. This derivative dataset was processed using the Mother of All BCI Benchmarks (MOABB) framework and is designed to support BCI research and algorithm development.\",\n  \"methods_description\": \"EEG data were acquired from 30 healthy participants using a 32-channel BioSemi ActiveTwo system with sintered Ag/AgCl active electrodes in a biosemi32 montage, sampled at 512 Hz with CMS/DRL reference and 50 Hz line frequency. Participants performed motor imagery tasks (left-hand grasping vs. rest) cued by auditory stimuli in a synchronous, offline paradigm. Each subject completed two experimental runs with 25 trials each (50 trials total), with 6-second trial intervals. Pupillometry data were recorded concurrently. Signal processing included common spatial pattern (CSP) feature extraction with bandpass filtering (8-30 Hz) and linear discriminant analysis (LDA) classification, evaluated using 10-fold within-subject cross-validation.\",\n  \"license\": \"CC0 1.0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"David Rozado\": {},\n    \"Andreas Duenser\": {},\n    \"Ben Howell\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"motor imagery\"\n    },\n    {\n      \"term\": \"Brain-Computer Interfaces\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D062207\"\n    },\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"pupillometry\"\n    },\n    {\n      \"term\": \"Electroencephalography\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D004569\"\n    },\n    {\n      \"term\": \"common spatial patterns\"\n    },\n    {\n      \"term\": \"BCI paradigm\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.1371/journal.pone.0121262\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"10.7910/DVN/28932\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/nm000148\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/nm000148\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.21105/joss.01896\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.1038/s41597-019-0104-8\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"EEG Dataset\",\n  \"modalities\": [\n    \"eeg\"\n  ],\n  \"sizes\": [\n    \"3.5 GB (123 files)\"\n  ],\n  \"formats\": [\n    \".bdf\",\n    \".html\",\n    \".json\",\n    \".md\",\n    \".tsv\",\n    \".xdf\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"source_hash\": \"7b65a4823258f78c69681e370ea82603f89d453e24f7c6cdc18aa466a9915c84\"\n}","last_activity_at":"2026-08-16 13:25:18","source":null,"source_id":null,"subject_count":30,"modalities":"eeg","age_min":38,"age_max":38,"file_size":3452375503,"total_files":583,"tasks":"imagery","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"David Rozado, Andreas Duenser, Ben Howell","license":"CC0 1.0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000148-blue)](https://doi.org/10.82901/nemar.nm000148)\n\n# Motor imagery BCI dataset with pupillometry augmentation\n\nMotor imagery BCI dataset with pupillometry augmentation.\n\n## Dataset Overview\n\n- **Code**: Rozado2015\n- **Paradigm**: imagery\n- **DOI**: 10.1371/journal.pone.0121262\n- **Subjects**: 30\n- **Sessions per subject**: 1\n- **Events**: left_hand=1, rest=2\n- **Trial interval**: [0.0, 6.0] s\n- **Runs per session**: 2\n- **File format**: XDF\n\n## Acquisition\n\n- **Sampling rate**: 512.0 Hz\n- **Number of channels**: 32\n- **Channel types**: eeg=32\n- **Montage**: biosemi32\n- **Hardware**: BioSemi ActiveTwo\n- **Reference**: CMS/DRL\n- **Sensor type**: active\n- **Line frequency**: 50.0 Hz\n- **Cap manufacturer**: BioSemi\n- **Electrode material**: sintered Ag/AgCl\n\n## Participants\n\n- **Number of subjects**: 30\n- **Health status**: healthy\n- **Age**: mean=38.0, std=9.69, min=15, max=61\n- **Gender distribution**: male=15, female=15\n- **Handedness**: {'right': 27, 'left': 3}\n\n## Experimental Protocol\n\n- **Paradigm**: imagery\n- **Task type**: left hand grasping imagery vs rest\n- **Number of classes**: 2\n- **Class labels**: left_hand, rest\n- **Trial duration**: 6.0 s\n- **Study design**: Motor imagery with pupillometry augmentation\n- **Feedback type**: none\n- **Stimulus type**: auditory cue\n- **Stimulus modalities**: auditory\n- **Primary modality**: auditory\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  rest\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Rest\n\n```\n## Paradigm-Specific Parameters\n\n- **Detected paradigm**: motor_imagery\n- **Imagery tasks**: left hand grasping, rest\n- **Imagery duration**: 6.0 s\n\n## Data Structure\n\n- **Blocks per session**: 2\n- **Block duration**: 300.0 s\n- **Trials context**: 2 experiments of 25 trials each (50 trials total per subject). Each experiment is stored as one XDF file.\n\n## Signal Processing\n\n- **Classifiers**: LDA\n- **Feature extraction**: CSP, pupil_diameter\n- **Frequency bands**: bandpass=[8.0, 30.0] Hz\n- **Spatial filters**: CSP\n\n## Cross-Validation\n\n- **Method**: 10-fold\n- **Folds**: 10\n- **Evaluation type**: within_subject\n\n## BCI Application\n\n- **Environment**: lab\n- **Online feedback**: False\n\n## Tags\n\n- **Pathology**: healthy\n- **Modality**: auditory\n- **Type**: motor_imagery\n\n## Documentation\n\n- **DOI**: 10.1371/journal.pone.0121262\n- **License**: CC0 1.0\n- **Investigators**: David Rozado, Andreas Duenser, Ben Howell\n- **Senior author**: David Rozado\n- **Institution**: CSIRO\n- **Department**: Digital Productivity Flagship\n- **Country**: AU\n- **Repository**: Harvard Dataverse\n- **Data URL**: https://doi.org/10.7910/DVN/28932\n- **Publication year**: 2015\n- **Keywords**: motor imagery, BCI, pupillometry, EEG, brain-computer interface\n\n## References\n\nD. Rozado, T. Duenser, and B. Gruen, \"Improving the performance of an EEG-based motor imagery brain computer interface using task evoked changes in pupil diameter,\" PLoS ONE, vol. 10, no. 3, e0121262, 2015. DOI: 10.1371/journal.pone.0121262\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. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8\n\n---\nGenerated by MOABB 1.5.0 (Mother of All BCI Benchmarks)\nhttps://github.com/NeuroTechX/moabb\n","bids_version":"1.9.0","sessions_count":1,"publish_date":"2026-04-29 22:29:02","embedding_dirty":0,"license_tier":"public","zarr_status":"ready","zarr_converted_at":"2026-09-04 12:39:11","zarr_store_count":60,"zarr_index_etag":"c3f80d4084a476bd5b029dfed6f64199","zarr_source_commit":"9b1afd1d36ea1efec38f8767e4102a3cbf505b12","archive_status":"ready","archive_size":2488766108,"archive_retry_count":0,"records_status":"ready","archive_skip_reason":null,"zarr_errors":0,"zarr_failure_count":0,"zarr_deterministic":0,"zarr_failed_at":null,"num_dataset_citations":0,"num_datapaper_citations":37,"n_channels":32,"electrode_system":"10-10","has_hed":1,"hed_version":"8.4.0","is_exemplar":0,"bytes_present":3451928667,"data_complete":1,"withdrawn_at":null,"withdrawn_reason":null,"archive_complete":1,"archive_absent_files":0,"archive_declared_files":583,"zarr_pool_breaks":0,"total_recording_duration":20555,"recording_duration_min":319,"recording_duration_max":627,"recording_count":60,"recordings_unavailable":0,"recordings_measured":60,"channel_count_min":32,"channel_count_max":32,"sampling_frequency":512,"power_line_frequency":50,"eeg_reference":"CMS/DRL","placement_scheme":"BioSemi 32-channel cap","sweep_stamps":"{\"enrichment_updated_at\":\"2026-08-18 18:11:11\",\"metadata_updated_at\":\"2026-08-18 18:13:26\",\"archive_checked_at\":\"2026-08-18 18:21:41\",\"zarr_checked_at\":\"2026-06-07 17:58:22\",\"records_checked_at\":\"2026-08-18 18:18:35\",\"citations_updated_at\":\"2026-09-08 03:00:48\",\"channel_montage_checked_at\":\"2026-06-28 22:52:49\",\"hed_checked_at\":\"2026-06-30 04:12:15\",\"data_checked_at\":null,\"availability_report_at\":\"2026-08-19 03:01:12\",\"signal_defaults_at\":\"2026-09-02 11:39:05\",\"recording_stats_at\":\"2026-09-05 03:00:42\",\"zarr_verify_attempted_at\":\"2026-09-08 03:01:02\",\"zarr_verified_at\":\"2026-09-08 03:01:02\",\"zarr_verified_commit\":\"9b1afd1d36ea1efec38f8767e4102a3cbf505b12\",\"zarr_verify_status\":\"verified\",\"zarr_verify_examples\":[],\"zarr_verify_sampled\":40.0,\"zarr_verify_checked\":40.0,\"zarr_verify_checked_channels\":40.0,\"zarr_verify_checked_duration\":40.0,\"zarr_verify_checked_rate\":40.0,\"zarr_verify_unchecked\":0.0,\"zarr_verify_mismatch_count\":0.0,\"zarr_verify_examples_truncated\":0.0}","participants":30,"num_citations":37,"latest_version":"v1.0.2","zarr_verify_status":"verified","zarr_verified_at":"2026-09-08 03:01:02","owner_username":"bruaristimunha","owner_github":"bruAristimunha","file_size_formatted":"3.22 GB","zarr_data_failures":null,"zarr_index_url":"https://zarr.nemar.org/nm000148/zarr/index.json","attestation_deposit_type":null,"attestation_key_status":null,"attestation_deidentified":null,"attestation_no_duplicate":null,"attestation_upstream_source":null,"attestation_accepted_at":null}}