R.I.P.
π»
Ghosted
Dataset of an EEG-based BCI experiment in Virtual Reality and on a Personal Computer
March 27, 2019 Β· Declared Dead Β· π arXiv.org
Authors
GrΓ©goire Cattan, A. Andreev, P. Rodrigues, M. Congedo
arXiv ID
1903.11297
Category
cs.HC: Human-Computer Interaction
Citations
4
Venue
arXiv.org
Repository
https://github.com/plcrodrigues/py.VR.EEG.2018-GIPSA
Last Checked
1 month ago
Abstract
We describe the experimental procedures for a dataset that we have made publicly available at https://doi.org/10.5281/zenodo.2605204 in mat (Mathworks, Natick, USA) and csv formats. This dataset contains electroencephalographic recordings on 21 subjects doing a visual P300 experiment on PC (personal computer) and VR (virtual reality). The visual P300 is an event-related potential elicited by a visual stimulation, peaking 240-600 ms after stimulus onset. The experiment was designed in order to compare the use of a P300-based brain-computer interface on a PC and with a virtual reality headset, concerning the physiological, subjective and performance aspects. The brain-computer interface is based on electroencephalography (EEG). EEG were recorded thanks to 16 electrodes. The virtual reality headset consisted of a passive head-mounted display, that is, a head-mounted display which does not include any electronics at the exception of a smartphone. This experiment was carried out at GIPSA-lab (University of Grenoble Alpes, CNRS, Grenoble-INP) in 2018, and promoted by the IHMTEK Company (Interaction Homme-Machine Technologie). The study was approved by the Ethical Committee of the University of Grenoble Alpes (Comit{Γ©} d'Ethique pour la Recherche Non-Interventionnelle). Python code for manipulating the data is available at https://github.com/plcrodrigues/py.VR.EEG.2018-GIPSA. The ID of this dataset is VR.EEG.2018-GIPSA.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π 404 Not Found
R.I.P.
π
404 Not Found
Deep High-Resolution Representation Learning for Visual Recognition
R.I.P.
π
404 Not Found
HuggingFace's Transformers: State-of-the-art Natural Language Processing
R.I.P.
π
404 Not Found
CCNet: Criss-Cross Attention for Semantic Segmentation
R.I.P.
π
404 Not Found