{"dataset":{"id":"242","dataset_id":"nm000209","name":"Motor imagery + spatial attention dataset from Forenzo & He 2023","description":"This dataset contains 64-channel EEG recordings from 25 healthy human subjects performing a motor imagery brain-computer interface (BCI) task combined with overt spatial attention, across 5 sessions per subject. Participants imagined left- or right-hand movements while receiving continuous cursor feedback, enabling study of integrated motor imagery and spatial attention paradigms for BCI control. The dataset was converted to BIDS format using MOABB from source data originally collected at Carnegie Mellon University.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000209","concept_doi":"10.82901/nemar.nm000209","latest_version_doi":"10.82901/nemar.nm000209.v1.0.4","created_at":"2026-03-24 03:19:03","updated_at":"2026-08-20 19:21:44","zenodo_concept_id":"20518411","is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"Motor imagery + spatial attention dataset from Forenzo & He 2023\",\n  \"description\": \"This dataset contains 64-channel EEG recordings from 25 healthy human subjects performing a motor imagery brain-computer interface (BCI) task combined with overt spatial attention, across 5 sessions per subject. Participants imagined left- or right-hand movements while receiving continuous cursor feedback, enabling study of integrated motor imagery and spatial attention paradigms for BCI control. The dataset was converted to BIDS format using MOABB from source data originally collected at Carnegie Mellon University.\",\n  \"methods_description\": \"EEG data were recorded at 1000 Hz using a 64-channel Neuroscan Quik-Cap with SynAmps 2/RT amplifiers, referenced between Cz and CPz, with online lowpass filtering at 200 Hz and 60 Hz notch filtering. Each subject completed 5 sessions with 3 motor imagery runs per session, performing left-hand or right-hand imagery trials of 6 seconds duration with cursor-based visual feedback in a synchronous online BCI paradigm.\",\n  \"license\": \"CC-BY-4.0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"Dylan Forenzo\": {\n      \"orcid\": \"0000-0002-2661-7434\",\n      \"affiliations\": [\n        {\n          \"name\": \"Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA\"\n        }\n      ]\n    },\n    \"Yixuan Liu\": {},\n    \"Jeehyun Kim\": {\n      \"orcid\": \"0009-0006-1159-589X\",\n      \"affiliations\": [\n        {\n          \"name\": \"Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA\"\n        }\n      ]\n    },\n    \"Yidan Ding\": {},\n    \"Taehyung Yoon\": {\n      \"orcid\": \"0009-0006-7347-564X\",\n      \"affiliations\": [\n        {\n          \"name\": \"Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA\"\n        }\n      ]\n    },\n    \"Bin He\": {\n      \"orcid\": \"0000-0003-2944-8602\",\n      \"affiliations\": [\n        {\n          \"name\": \"Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA\"\n        }\n      ]\n    }\n  },\n  \"keywords\": [\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"motor imagery\"\n    },\n    {\n      \"term\": \"Brain-Computer Interfaces\",\n      \"subject_scheme\": \"MeSH\",\n      \"scheme_uri\": \"https://id.nlm.nih.gov/mesh/\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D062207\"\n    },\n    {\n      \"term\": \"spatial attention\"\n    },\n    {\n      \"term\": \"cursor control\"\n    },\n    {\n      \"term\": \"healthy volunteers\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.1109/TBME.2023.3298957\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n 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session**: 3\n- **File format**: MAT\n\n## Acquisition\n\n- **Sampling rate**: 1000.0 Hz\n- **Number of channels**: 64\n- **Channel types**: eeg=64\n- **Montage**: standard_1005\n- **Hardware**: Neuroscan Quik-Cap 64-ch, SynAmps 2/RT\n- **Reference**: between Cz and CPz\n- **Sensor type**: Ag/AgCl\n- **Line frequency**: 60.0 Hz\n- **Online filters**: {'lowpass': 200, 'notch_hz': 60}\n\n## Participants\n\n- **Number of subjects**: 25\n- **Health status**: healthy\n- **Age**: mean=25.5\n- **Gender distribution**: female=10, male=15\n- **Handedness**: right-handed (24 of 25)\n- **BCI experience**: mixed (19 naive, 6 experienced)\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**: 6.0 s\n- **Study design**: 5-session BCI study with motor imagery (MI), overt spatial attention (OSA), and combined (MIOSA) tasks\n- **Feedback type**: cursor\n- **Stimulus type**: continuous pursuit\n- **Stimulus modalities**: visual\n- **Primary modality**: visual\n- **Synchronicity**: synchronous\n- **Mode**: online\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- **Imagery duration**: 6.0 s\n\n## Data Structure\n\n- **Trials**: 1875\n- **Trials context**: 25 subjects x 5 sessions x 3 MI runs x 5 trials\n\n## Signal Processing\n\n- **Classifiers**: linear_classifier\n- **Feature extraction**: AR_spectral_estimation, alpha_bandpower\n- **Frequency bands**: alpha=[8.0, 13.0] Hz\n- **Spatial filters**: Laplacian\n\n## Cross-Validation\n\n- **Evaluation type**: within_subject\n\n## BCI Application\n\n- **Applications**: cursor_control\n- **Environment**: laboratory\n- **Online feedback**: True\n\n## Tags\n\n- **Pathology**: Healthy\n- **Modality**: Motor\n- **Type**: Research\n\n## Documentation\n\n- **DOI**: 10.1109/TBME.2023.3298957\n- **License**: CC-BY-4.0\n- **Investigators**: Dylan Forenzo, Yixuan Liu, Jeehyun Kim, Yidan Ding, Taehyung Yoon, Bin He\n- **Institution**: Carnegie Mellon University\n- **Department**: Department of Biomedical Engineering\n- **Country**: US\n- **Data URL**: https://kilthub.cmu.edu/articles/dataset/23677098\n- **Publication year**: 2023\n\n## References\n\nForenzo, D., & He, B. (2024). Integrating simultaneous motor imagery and spatial attention for EEG-BCI control. IEEE Trans. Biomed. Eng., 71(1), 282-294. https://doi.org/10.1109/TBME.2023.3298957\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":2,"publish_date":"2026-03-24 03:19:03","embedding_dirty":0,"license_tier":"attribution","zarr_status":"ready","zarr_converted_at":"2026-09-04 10:39:59","zarr_store_count":150,"zarr_index_etag":"1d828d4d14dca897521057a875317fea","zarr_source_commit":"6972a8a4333660b5e1bfbb38ad90d6552dbe7ef6","archive_status":null,"archive_size":3781492320,"archive_retry_count":0,"records_status":"ready","archive_skip_reason":"dataset 183.9 GB exceeds 100.0 GB archive limit; use direct download","zarr_errors":0,"zarr_failure_count":0,"zarr_deterministic":0,"zarr_failed_at":null,"num_dataset_citations":0,"num_datapaper_citations":22,"n_channels":64,"electrode_system":"10-10","has_hed":1,"hed_version":"8.4.0","is_exemplar":0,"bytes_present":197454149691,"data_complete":1,"withdrawn_at":null,"withdrawn_reason":null,"archive_complete":null,"archive_absent_files":null,"archive_declared_files":null,"zarr_pool_breaks":0,"total_recording_duration":27334,"recording_duration_min":130,"recording_duration_max":237,"recording_count":150,"recordings_unavailable":0,"recordings_measured":150,"channel_count_min":64,"channel_count_max":64,"sampling_frequency":1000,"power_line_frequency":60,"eeg_reference":"between Cz and CPz","placement_scheme":"10-05 system","sweep_stamps":"{\"enrichment_updated_at\":\"2026-08-20 19:21:00\",\"metadata_updated_at\":\"2026-08-20 19:21:43\",\"archive_checked_at\":\"2026-08-20 19:21:55\",\"zarr_checked_at\":\"2026-06-07 17:58:30\",\"records_checked_at\":\"2026-08-20 19:22:38\",\"citations_updated_at\":\"2026-09-08 03:00:48\",\"channel_montage_checked_at\":\"2026-06-28 22:58:00\",\"hed_checked_at\":\"2026-06-30 07:29:02\",\"data_checked_at\":null,\"availability_report_at\":\"2026-07-23 01:08:03\",\"signal_defaults_at\":\"2026-09-02 11:44:29\",\"recording_stats_at\":\"2026-09-05 03:01:27\"}","participants":25,"num_citations":22,"latest_version":"v1.0.4","zarr_verify_status":null,"zarr_verified_at":null,"owner_username":"bruaristimunha","owner_github":"bruAristimunha","file_size_formatted":"184 GB","zarr_data_failures":null,"zarr_index_url":"https://zarr.nemar.org/nm000209/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}}