Neuroergonomic 2021 dataset.
Code: Hinss2021 Paradigm: rstate DOI: 10.1038/s41597-022-01898-y Subjects: 15 Sessions per subject: 2 Events: rs=1, easy=2, medium=3, diff=4 Trial interval: [0, 2] s File format: set
Sampling rate: 500.0 Hz Number of channels: 62 Channel types: eeg=62 Channel names: AF3, AF4, AF7, AF8, AFz, C1, C2, C3, C4, C5, C6, CP1, CP2, CP3, CP4, CP5, CP6, CPz, F1, F2, F3, F4, F5, F6, F7, F8, FC1, FC2, FC3, FC4, FC5, FC6, FCz, FT10, FT7, FT8, FT9, Fp1, Fp2, Fz, O1, O2, Oz, P1, P2, P3, P4, P5, P6, P7, P8, PO3, PO4, PO7, PO8, POz, Pz, T7, T8, TP7, TP8 Montage: standard_1020 Hardware: ActiCHamp (Brain Products Gmbh) Reference: Fpz Sensor type: active Ag/AgCl Line frequency: 50.0 Hz Impedance threshold: 25 kOhm Auxiliary channels: ecg
Number of subjects: 15 Health status: healthy Age: mean=23.9 Gender distribution: female=11, male=18
Paradigm: rstate Number of classes: 4 Class labels: rs, easy, medium, diff Study design: Passive BCI neuroergonomics dataset with resting state and 3 difficulty levels of MATB-II task (easy, medium, difficult). The MOABB loader provides resting state and MATB conditions only. Feedback type: none Stimulus type: visual display Training/test split: False
Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser
rs ├─ Experiment-structure └─ Rest
easy ├─ Experiment-structure └─ Label/easy
medium ├─ Experiment-structure └─ Label/medium
diff ├─ Experiment-structure └─ Label/difficult
Detected paradigm: resting_state
Trials: 90 Trials context: total
Data state: raw Preprocessing applied: False
Classifiers: MDM, Riemannian Feature extraction: Bandpower, Covariance/Riemannian, ICA Frequency bands: alpha=[8.0, 13.0] Hz; theta=[4.0, 8.0] Hz
Method: 5-fold Folds: 5 Evaluation type: cross_subject, cross_session, transfer_learning
Accuracy: 70.67%
Applications: neuroergonomics, mental_workload_estimation Environment: laboratory
Pathology: Healthy Modality: Cognitive Type: Research
DOI: 10.1038/s41597-022-01898-y License: CC-BY-SA-4.0 Investigators: Marcel F. Hinss, Emilie S. Jahanpour, Bertille Somon, Lou Pluchon, Frédéric Dehais, Raphaëlle N. Roy Senior author: Raphaëlle N. Roy Contact: marcel.hinss@isae-supaero.fr Institution: ISAE-SUPAERO, Université de Toulouse Department: Department of Information Processing and Systems Address: Toulouse, France Country: FR Repository: Zenodo Data URL: https://doi.org/10.5281/zenodo.6874128 Publication year: 2023 Funding: ERASMUS program; ANITI (Artificial and Natural Intelligence Toulouse Institute) Ethics approval: Comité d'Éthique de la Recherche (CER), Université de Toulouse (CER number 2021-342) Acknowledgements: This research was supported in part by the ERASMUS program (which funded Mr Hinss' internship), and by ANITI (Artificial and Natural Intelligence Toulouse Institute), Toulouse, France. How to acknowledge: Please cite: Hinss et al. (2023). Open multi-session and multi-task EEG cognitive dataset for passive brain-computer interface applications. Scientific Data, 10, 85. https://doi.org/10.1038/s41597-022-01898-y
.. [Hinss2021] M. Hinss, B. Somon, F. Dehais & R. N. Roy (2021) Open EEG Datasets for Passive Brain-Computer Interface Applications: Lacks and Perspectives. IEEE Neural Engineering Conference.
.. [Hinss2023] M. F. Hinss, et al. (2023) An EEG dataset for cross-session mental workload estimation: Passive BCI competition of the Neuroergonomics Conference 2021. Scientific Data, 10, 85. https://doi.org/10.1038/s41597-022-01898-y Appelhoff, 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
Pernet, 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
Generated by MOABB 1.5.0 (Mother of All BCI Benchmarks) https://github.com/NeuroTechX/moabb