{"dataset":{"id":"252","dataset_id":"nm000219","name":"BNCI 2020-002 Attention Shift (Covert Spatial Attention) dataset","description":"A gaze-independent brain-computer interface dataset based on covert spatial attention shifts for binary communication. This EEG dataset comprises recordings from 18 healthy participants performing a visual attention task with N2pc event-related potential detection. The paradigm uses colored visual stimuli (green crosses for 'yes', red crosses for 'no') presented while maintaining central gaze fixation, achieving 88.5% mean accuracy with canonical correlation analysis classification.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000219","concept_doi":"10.82901/nemar.nm000219","latest_version_doi":"10.82901/nemar.nm000219.v1.0.2","created_at":"2026-03-24 08:25:18","updated_at":"2026-08-18 21:16:46","zenodo_concept_id":"20519043","is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"enriched\",\n  \"title\": \"BNCI 2020-002 Attention Shift (Covert Spatial Attention) dataset\",\n  \"description\": \"A gaze-independent brain-computer interface dataset based on covert spatial attention shifts for binary communication. This EEG dataset comprises recordings from 18 healthy participants performing a visual attention task with N2pc event-related potential detection. The paradigm uses colored visual stimuli (green crosses for 'yes', red crosses for 'no') presented while maintaining central gaze fixation, achieving 88.5% mean accuracy with canonical correlation analysis classification.\",\n  \"methods_description\": \"EEG data were acquired from 18 healthy participants (8 male, 10 female; mean age 27 years) using a 30-channel BrainAmp DC amplifier with Ag/AgCl electrodes in extended 10-20 montage, sampled at 250 Hz with right mastoid reference. Online highpass filtering at 0.1 Hz was applied during acquisition. Stimuli consisted of colored crosses (250 ms duration) presented with 850 ms stimulus onset asynchrony (jittered 0-250 ms). Data were preprocessed with re-referencing to bilateral mastoid average, 4th-order Butterworth bandpass filtering (1.0-12.5 Hz), downsampling to 50 Hz, and epoching from stimulus onset to 750 ms post-stimulus.\",\n  \"license\": \"CC-BY-4.0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"Christoph Reichert\": {},\n    \"Igor Fabian Tellez Ceja\": {},\n    \"Catherine M. Sweeney-Reed\": {},\n    \"Hans-Jochen Heinze\": {},\n    \"Hermann Hinrichs\": {},\n    \"Stefan Dürschmid\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"brain-computer interface\"\n    },\n    {\n      \"term\": \"covert spatial attention\"\n    },\n    {\n      \"term\": \"visual spatial attention\"\n    },\n    {\n      \"term\": \"N2pc\"\n    },\n    {\n      \"term\": \"Event-Related Potentials\"\n    },\n    {\n      \"term\": \"canonical correlation analysis\"\n    },\n    {\n      \"term\": \"gaze-independent BCI\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.3389/fnins.2020.591777\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/nm000219\",\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      \"identifier\": \"https://nemar.org/dataset/nm000219\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"German Ministry of Education and Research (BMBF)\",\n      \"award_number\": \"13GW0095D\",\n      \"award_title\": \"Research Campus STIMULATE\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"EEG Dataset\",\n  \"modalities\": [\n    \"eeg\"\n  ],\n  \"sizes\": [\n    \"2.0 GB (37 files)\"\n  ],\n  \"formats\": [\n    \".bdf\",\n    \".json\",\n    \".mat\",\n    \".md\",\n    \".tsv\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"source_hash\": \"777e6142d284c23e6cfbb9e4f14c66541237bbca92a850fa0c3920e8f5b864e3\"\n}","last_activity_at":"2026-08-16 13:32:17","source":null,"source_id":null,"subject_count":18,"modalities":"eeg","age_min":18,"age_max":38,"file_size":1954127439,"total_files":227,"tasks":"p300","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Christoph Reichert, Igor Fabian Tellez Ceja, Catherine M. Sweeney-Reed, Hans-Jochen Heinze, Hermann Hinrichs, Stefan Dürschmid","license":"CC-BY-4.0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000219-blue)](https://doi.org/10.82901/nemar.nm000219)\n\n# BNCI 2020-002 Attention Shift (Covert Spatial Attention) dataset\n\nBNCI 2020-002 Attention Shift (Covert Spatial Attention) dataset.\n\n## Dataset Overview\n\n- **Code**: BNCI2020-002\n- **Paradigm**: p300\n- **DOI**: 10.3389/fnins.2020.591777\n- **Subjects**: 18\n- **Sessions per subject**: 1\n- **Events**: NonTarget=1, Target=2\n- **Trial interval**: [0, 16] s\n- **File format**: MAT\n\n## Acquisition\n\n- **Sampling rate**: 250.0 Hz\n- **Number of channels**: 30\n- **Channel types**: eeg=30, eog=2\n- **Channel names**: C3, C4, CP1, CP2, Cz, F3, F4, F7, F8, FC1, FC2, Fp1, Fp2, Fz, HEOG, IZ, LMAST, O10, O9, Oz, P3, P4, P7, P8, PO3, PO4, PO7, PO8, Pz, T7, T8, VEOG\n- **Montage**: extended 10-20\n- **Hardware**: BrainAmp DC Amplifier\n- **Reference**: right mastoid\n- **Sensor type**: Ag/AgCl electrodes\n- **Line frequency**: 50.0 Hz\n- **Online filters**: 0.1 Hz highpass\n- **Cap manufacturer**: Brain Products GmbH\n- **Auxiliary channels**: EOG (2 ch, horizontal, vertical)\n\n## Participants\n\n- **Number of subjects**: 18\n- **Health status**: healthy\n- **Age**: mean=27.0, min=19.0, max=38.0\n- **Gender distribution**: male=8, female=10\n- **Species**: human\n\n## Experimental Protocol\n\n- **Paradigm**: p300\n- **Task type**: binary decision\n- **Number of classes**: 2\n- **Class labels**: NonTarget, Target\n- **Feedback type**: visual (yes/no text)\n- **Stimulus type**: colored crosses (green + and red x)\n- **Stimulus modalities**: visual\n- **Primary modality**: visual\n- **Synchronicity**: synchronous\n- **Mode**: online\n- **Training/test split**: True\n- **Instructions**: Respond to yes/no questions by shifting attention to green cross (yes) or red cross (no) while maintaining central gaze fixation\n- **Stimulus presentation**: duration_ms=250, soa_ms=850 (jittered by 0-250 ms), stimuli_per_trial=10\n\n## HED Event Annotations\n\nSchema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n\n```\n  NonTarget\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Non-target\n\n  Target\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Target\n\n```\n## Paradigm-Specific Parameters\n\n- **Detected paradigm**: p300\n- **Number of targets**: 2\n- **Number of repetitions**: 10\n- **Stimulus onset asynchrony**: 850.0 ms\n\n## Data Structure\n\n- **Trials**: 24\n- **Blocks per session**: 7\n- **Trials context**: per_block\n\n## Preprocessing\n\n- **Data state**: raw\n- **Preprocessing applied**: False\n- **Steps**: re-referenced to average of left and right mastoid, 4th order zero-phase IIR Butterworth bandpass filter (1.0-12.5 Hz), resampled to 50 Hz, epoched from stimulus onset to 750 ms after\n- **Highpass filter**: 1.0 Hz\n- **Lowpass filter**: 12.5 Hz\n- **Bandpass filter**: [1.0, 12.5]\n- **Filter type**: Butterworth IIR\n- **Filter order**: 4\n- **Re-reference**: average of left and right mastoid\n- **Downsampled to**: 50.0 Hz\n- **Epoch window**: [0.0, 0.75]\n\n## Signal Processing\n\n- **Classifiers**: Canonical Correlation Analysis (CCA)\n- **Feature extraction**: N2pc, ERP, Canonical difference waves\n- **Spatial filters**: CCA spatial filters\n\n## Cross-Validation\n\n- **Method**: leave-one-out cross-validation (LOOCV)\n- **Evaluation type**: within_subject\n\n## Performance (Original Study)\n\n- **Accuracy**: 88.5%\n- **Itr**: 3.02 bits/min\n- **Std Accuracy**: 7.8\n- **Min Accuracy**: 70.8\n- **Max Accuracy**: 90.3\n\n## BCI Application\n\n- **Applications**: communication, binary decision\n- **Environment**: laboratory\n- **Online feedback**: True\n\n## Tags\n\n- **Pathology**: Healthy\n- **Modality**: Visual\n- **Type**: Attention\n\n## Documentation\n\n- **Description**: Gaze-independent brain-computer interface based on covert spatial attention shifts for binary (yes/no) communication\n- **DOI**: 10.3389/fnins.2020.591777\n- **Associated paper DOI**: 10.3389/fnins.2020.591777\n- **License**: CC-BY-4.0\n- **Investigators**: Christoph Reichert, Igor Fabian Tellez Ceja, Catherine M. Sweeney-Reed, Hans-Jochen Heinze, Hermann Hinrichs, Stefan Dürschmid\n- **Senior author**: Stefan Dürschmid\n- **Contact**: christoph.reichert@lin-magdeburg.de\n- **Institution**: Leibniz Institute for Neurobiology\n- **Department**: Department of Behavioral Neurology\n- **Address**: Magdeburg, Germany\n- **Country**: Germany\n- **Repository**: BNCI Horizon\n- **Data URL**: http://bnci-horizon-2020.eu/database/data-sets\n- **Publication year**: 2020\n- **Funding**: German Ministry of Education and Research (BMBF) within the Research Campus STIMULATE under grant number 13GW0095D\n- **Ethics approval**: Ethics Committee of the Otto-von-Guericke University, Magdeburg\n- **Keywords**: visual spatial attention, brain-computer interface, stimulus features, N2pc, canonical correlation analysis, gaze-independent, BCI\n\n## References\n\nReichert, C., Tellez-Ceja, I. F., Schwenker, F., Rusnac, A.-L., Curio, G., Aust, L., & Hinrichs, H. (2020). Impact of Stimulus Features on the Performance of a Gaze-Independent Brain-Computer Interface Based on Covert Spatial Attention Shifts. Frontiers in Neuroscience, 14, 591777. https://doi.org/10.3389/fnins.2020.591777\n\nNotes\n\n.. versionadded:: 1.3.0\n\nThis dataset uses a covert spatial attention paradigm with N2pc ERP detection, which is different from traditional P300 or motor imagery paradigms. The paradigm is designed for gaze-independent BCI control, making it suitable for users who cannot control eye movements.\n\nSee Also\n\nBNCI2015_009 : AMUSE auditory spatial P300 paradigm BNCI2015_010 : RSVP visual P300 paradigm\n\nExamples\n\n>>> from moabb.datasets import BNCI2020_002 >>> dataset = BNCI2020_002() >>> dataset.subject_list [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]\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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