{"dataset":{"id":"229","dataset_id":"nm000196","name":"c-VEP dataset from Thielen et al. (2015)","description":"This dataset comprises preprocessed EEG recordings from 12 healthy participants performing a brain-computer interface task based on broad-band visually evoked potentials (BBVEPs). Participants controlled a 6×6 matrix speller by attending to target symbols while cells were stimulated with pseudo-random Gold code sequences. The study demonstrates a novel reconvolution approach for template generation that achieves 86% online accuracy and an information transfer rate of 48 bits per minute.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000196","concept_doi":"10.82901/nemar.nm000196","latest_version_doi":"10.82901/nemar.nm000196.v1.0.3","created_at":"2026-03-24 00:46:13","updated_at":"2026-08-18 21:10:09","zenodo_concept_id":"20500887","is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"c-VEP dataset from Thielen et al. (2015)\",\n  \"description\": \"This dataset comprises preprocessed EEG recordings from 12 healthy participants performing a brain-computer interface task based on broad-band visually evoked potentials (BBVEPs). Participants controlled a 6×6 matrix speller by attending to target symbols while cells were stimulated with pseudo-random Gold code sequences. The study demonstrates a novel reconvolution approach for template generation that achieves 86% online accuracy and an information transfer rate of 48 bits per minute.\",\n  \"methods_description\": \"EEG data were acquired from 64 channels using a Biosemi ActiveTwo system at 2048 Hz sampling rate with CMS/DRL reference. Participants completed 3 runs of 36 trials each (4.2 seconds per trial). Data were preprocessed including downsampling to 360 Hz, linear de-trending, common average referencing, and bandpass filtering (5-100 Hz with notch at 50 Hz). Classification employed template matching with correlation and Canonical Correlation Analysis for spatial filtering.\",\n  \"license\": \"CC0-1.0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"Jordy Thielen\": {\n      \"orcid\": \"0000-0002-6264-0367\"\n    },\n    \"Philip van den Broek\": {},\n    \"Jason Farquhar\": {\n      \"orcid\": \"0000-0002-8560-0712\"\n    },\n    \"Peter Desain\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"Brain-Computer Interfaces\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D062207\"\n    },\n    {\n      \"term\": \"Evoked Potentials, Visual\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D005074\"\n    },\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"visual stimulation\"\n    },\n    {\n      \"term\": \"Gold codes\"\n    },\n    {\n      \"term\": \"speller BCI\"\n    },\n    {\n      \"term\": \"reconvolution\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.34973/1ecz-1232\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/nm000196\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.1371/journal.pone.0133797\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/nm000196\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"BrainGain Smart Mix Program of the Netherlands Ministry of Economic Affairs\"\n    },\n    {\n      \"funder_name\": \"Netherlands Ministry of Economic Affairs\",\n      \"award_title\": \"BrainGain Smart Mix Program\"\n    },\n    {\n      \"funder_name\": \"Netherlands Ministry of Education, Culture and Science\",\n      \"award_number\": \"SSM06011\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"EEG Dataset\",\n  \"modalities\": [\n    \"eeg\"\n  ],\n  \"sizes\": [\n    \"11.8 GB (73 files)\"\n  ],\n  \"formats\": [\n    \".bdf\",\n    \".gdf\",\n    \".json\",\n    \".loc\",\n    \".mat\",\n    \".md\",\n    \".tsv\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"source_hash\": \"a507d84e67dc92064526fb2068bf52ee69f42ffaf5edbfc8b0b4d7115bf44d69\"\n}","last_activity_at":"2026-08-16 13:29:27","source":null,"source_id":null,"subject_count":12,"modalities":"eeg","age_min":24,"age_max":24,"file_size":11857561091,"total_files":360,"tasks":"cvep","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Jordy Thielen, Philip van den Broek, Jason Farquhar, Peter Desain","license":"CC0-1.0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000196-blue)](https://doi.org/10.82901/nemar.nm000196)\n\n# c-VEP dataset from Thielen et al. (2015)\n\nc-VEP dataset from Thielen et al. (2015)\n\n## Dataset Overview\n\n- **Code**: Thielen2015\n- **Paradigm**: cvep\n- **DOI**: 10.34973/1ecz-1232\n- **Subjects**: 12\n- **Sessions per subject**: 1\n- **Events**: 1.0=101, 0.0=100\n- **Trial interval**: (0, 0.3) s\n- **Runs per session**: 3\n- **File format**: mat\n- **Data preprocessed**: True\n\n## Acquisition\n\n- **Sampling rate**: 2048.0 Hz\n- **Number of channels**: 64\n- **Channel types**: eeg=64\n- **Channel names**: AF3, AF4, AF7, AF8, AFz, C1, C2, C3, C4, C5, C6, CP1, CP2, CP3, CP4, CP5, CP6, CPz, Cz, F1, F2, F3, F4, F5, F6, F7, F8, FC1, FC2, FC3, FC4, FC5, FC6, FCz, FT7, FT8, Fp1, Fp2, Fpz, Fz, Iz, O1, O2, Oz, P1, P10, P2, P3, P4, P5, P6, P7, P8, P9, PO3, PO4, PO7, PO8, POz, Pz, T7, T8, TP7, TP8\n- **Montage**: standard_1020\n- **Hardware**: Biosemi ActiveTwo\n- **Reference**: CMS/DRL\n- **Sensor type**: EEG\n- **Line frequency**: 50.0 Hz\n- **Electrode type**: active\n\n## Participants\n\n- **Number of subjects**: 12\n- **Health status**: patients\n- **Clinical population**: Healthy\n- **Age**: mean=24.0, std=2.3\n- **Gender distribution**: male=4, female=8\n- **BCI experience**: naive\n- **Species**: human\n\n## Experimental Protocol\n\n- **Paradigm**: cvep\n- **Number of classes**: 2\n- **Class labels**: 1.0, 0.0\n- **Trial duration**: 4.2 s\n- **Study design**: 6x6 matrix speller BCI using modulated Gold codes for visual stimulation; participants focused on target symbols while cells flashed according to pseudo-random bit-sequences\n- **Feedback type**: visual\n- **Stimulus type**: pseudo-random noise-code\n- **Stimulus modalities**: visual\n- **Primary modality**: visual\n- **Synchronicity**: synchronous\n- **Mode**: online\n- **Training/test split**: False\n- **Instructions**: participants visually attended cells containing target symbols during stimulation\n\n## HED Event Annotations\n\nSchema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n\n```\n  1.0\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/intensity_1_0\n\n  0.0\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/intensity_0_0\n\n```\n## Paradigm-Specific Parameters\n\n- **Detected paradigm**: cvep\n- **Code type**: modulated Gold codes\n- **Code length**: 126\n- **Number of targets**: 36\n\n## Data Structure\n\n- **Trials**: 108\n- **Trials context**: 108 total per subject: 3 fixed-length copy-spelling runs x 36 trials per run, each trial 4.2 seconds (4 code cycles)\n\n## Preprocessing\n\n- **Data state**: preprocessed\n- **Preprocessing applied**: True\n- **Steps**: downsampling from 2048 Hz to 360 Hz, linear de-trending, common average referencing, spectral filtering\n- **Highpass filter**: 5 Hz\n- **Lowpass filter**: 100 Hz\n- **Bandpass filter**: {'band1': [5, 48], 'band2': [52, 100]}\n- **Re-reference**: car\n- **Downsampled to**: 360.0 Hz\n\n## Signal Processing\n\n- **Classifiers**: template matching, CCA\n- **Feature extraction**: correlation\n- **Spatial filters**: Canonical Correlation Analysis\n\n## Cross-Validation\n\n- **Method**: training-testing split\n- **Evaluation type**: within-subject\n\n## Performance (Original Study)\n\n- **Accuracy Fixed Length**: 86.0\n- **Itr Fixed Length**: 38.12\n- **Spm Fixed Length**: 6.93\n- **Accuracy Early Stopping**: 86.0\n- **Itr Early Stopping**: 48.37\n- **Spm Early Stopping**: 8.99\n\n## BCI Application\n\n- **Applications**: speller\n- **Environment**: laboratory\n- **Online feedback**: True\n\n## Tags\n\n- **Pathology**: Healthy\n- **Modality**: Visual\n- **Type**: Research\n\n## Documentation\n\n- **DOI**: 10.1371/journal.pone.0133797\n- **License**: CC0-1.0\n- **Investigators**: Jordy Thielen, Philip van den Broek, Jason Farquhar, Peter Desain\n- **Senior author**: Peter Desain\n- **Contact**: jordy.thielen@gmail.com; info@donders.ru.nl\n- **Institution**: Radboud University Nijmegen\n- **Department**: Donders Center for Cognition\n- **Country**: NL\n- **Repository**: GitHub\n- **Data URL**: https://public.data.ru.nl/dcc/DSC_2018.00047_553_v3\n- **Publication year**: 2015\n- **Funding**: BrainGain Smart Mix Program of the Netherlands Ministry of Economic Affairs; Netherlands Ministry of Education, Culture and Science (SSM06011)\n- **Ethics approval**: Ethical Committee of the Faculty of Social Sciences at the Radboud University Nijmegen\n- **Keywords**: Brain-Computer Interface, BCI, Broad-Band Visually Evoked Potentials, BBVEP, Gold codes, reconvolution, speller, visual stimulation\n\n## Abstract\n\nBrain-Computer Interfaces (BCIs) allow users to control devices and communicate by using brain activity only. BCIs based on broad-band visual stimulation can outperform BCIs using other stimulation paradigms. Visual stimulation with pseudo-random bit-sequences evokes specific Broad-Band Visually Evoked Potentials (BBVEPs) that can be reliably used in BCI for high-speed communication in speller applications. In this study, we report a novel paradigm for a BBVEP-based BCI that utilizes a generative framework to predict responses to broad-band stimulation sequences. In this study we designed a BBVEP-based BCI using modulated Gold codes to mark cells in a visual speller BCI. We defined a linear generative model that decomposes full responses into overlapping single-flash responses. These single-flash responses are used to predict responses to novel stimulation sequences, which in turn serve as templates for classification. The linear generative model explains on average 50% and up to 66% of the variance of responses to both seen and unseen sequences. In an online experiment, 12 participants tested a 6 × 6 matrix speller BCI. On average, an online accuracy of 86% was reached with trial lengths of 3.21 seconds. This corresponds to an Information Transfer Rate of 48 bits per minute (approximately 9 symbols per minute). This study indicates the potential to model and predict responses to broad-band stimulation. These predicted responses are proven to be well-suited as templates for a BBVEP-based BCI, thereby enabling communication and control by brain activity only.\n\n## Methodology\n\nThe study implements a novel BBVEP-based BCI using modulated Gold codes with a reconvolution approach for template generation. The reconvolution model decomposes responses into single-flash responses (short and long pulses) and predicts responses to unseen sequences. Two sets of Gold codes were used: set V for training (65 sequences) and set U for testing (65 sequences). Each sequence had 126 bits with duration of 1.05s. The classifier uses template matching with correlation, combined with Canonical Correlation Analysis for spatial filtering. Subset optimization (Platinum subset) selects the most distinguishable codes, and layout optimization arranges codes on the 6x6 grid to minimize cross-talk. An early stopping algorithm was implemented to reduce trial duration. Online experiments were conducted with 12 participants using a synchronous BCI paradigm.\n\n## References\n\nThielen, J. (Jordy), Jason Farquhar, Desain, P.W.M. (Peter) (2023): Broad-Band Visually Evoked Potentials: Re(con)volution in Brain-Computer Interfacing. Version 2. Radboud University. (dataset). DOI: https://doi.org/10.34973/1ecz-1232\n\nThielen, J., Van Den Broek, P., Farquhar, J., & Desain, P. (2015). Broad-Band visually evoked potentials: re(con)volution in brain-computer interfacing. PLOS ONE, 10(7), e0133797. DOI: https://doi.org/10.1371/journal.pone.0133797\n\nNotes\n\n.. versionadded:: 1.0.0\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. ","bids_version":"1.9.0","sessions_count":1,"publish_date":"2026-06-02 01:31:06","embedding_dirty":0,"license_tier":"public","zarr_status":"ready","zarr_converted_at":"2026-09-04 11:07:23","zarr_store_count":36,"zarr_index_etag":"0cb597c9e9c5ea032daa2bcae8a83d75","zarr_source_commit":"1a9c826048bc030befa68f54a721b1f36527499b","archive_status":"ready","archive_size":7239487063,"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":72,"n_channels":64,"electrode_system":"10-10","has_hed":1,"hed_version":"8.4.0","is_exemplar":0,"bytes_present":11833965192,"data_complete":1,"withdrawn_at":null,"withdrawn_reason":null,"archive_complete":1,"archive_absent_files":0,"archive_declared_files":360,"zarr_pool_breaks":0,"total_recording_duration":9434,"recording_duration_min":251,"recording_duration_max":322,"recording_count":36,"recordings_unavailable":0,"recordings_measured":36,"channel_count_min":64,"channel_count_max":64,"sampling_frequency":2048,"power_line_frequency":50,"eeg_reference":"CMS/DRL","placement_scheme":"10-20 system","sweep_stamps":"{\"enrichment_updated_at\":\"2026-08-18 18:11:16\",\"metadata_updated_at\":\"2026-08-18 21:10:07\",\"archive_checked_at\":\"2026-08-18 21:16:02\",\"zarr_checked_at\":\"2026-06-07 17:58:28\",\"records_checked_at\":\"2026-08-18 21:11:09\",\"citations_updated_at\":\"2026-09-07 03:00:47\",\"channel_montage_checked_at\":\"2026-06-28 22:57:16\",\"hed_checked_at\":\"2026-06-30 07:27:32\",\"data_checked_at\":null,\"availability_report_at\":\"2026-08-20 03:01:20\",\"signal_defaults_at\":\"2026-09-02 11:43:13\",\"recording_stats_at\":\"2026-09-05 03:01:13\"}","participants":12,"num_citations":72,"latest_version":"v1.0.3","zarr_verify_status":null,"zarr_verified_at":null,"owner_username":"bruaristimunha","owner_github":"bruAristimunha","file_size_formatted":"11.04 GB","zarr_data_failures":null,"zarr_index_url":"https://zarr.nemar.org/nm000196/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}}