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Dataset . 2018
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ZENODO
Dataset . 2018
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Brain Invaders Adaptive versus Non-Adaptive P300 Brain-Computer Interface dataset

Authors: Erwan Vaineau; Alexandre Barachant; Anton Andreev; Pedro L. C. Rodrigues; Grégoire Cattan; Marco Congedo;

Brain Invaders Adaptive versus Non-Adaptive P300 Brain-Computer Interface dataset

Abstract

Summary: This dataset contains electroencephalographic (EEG) recordings of 24 subjects doing a visual P300 Brain-Computer Interface experiment on PC. The visual P300 is an event-related potential elicited by 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 with and without adaptive calibration using Riemannian geometry. The brain-computer interface is based on electroencephalography (EEG). EEG data were recorded thanks to 16 electrodes. A full description of the experiment is available at https://hal.archives-ouvertes.fr/hal-02103098. Data were recorded during an experiment taking place in the GIPSA-lab, Grenoble, France, in 2013 (Congedo, 2013). Python code for manipulating the data is available at https://github.com/plcrodrigues/py.BI.EEG.2013-GIPSA. The ID of this dataset is BI.EEG.2013-GIPSA. Full description of the experiment and dataset: https://hal.archives-ouvertes.fr/hal-02103098 Principal Investigator: B.Sc. Erwan Vaineau, Ph.D. Alexandre Barachant Technical Supervisors: Eng. Anton Andreev, Eng. Pedro. L. C. Rodrigues, Eng. Grégoire Cattan Scientific Supervisor: Ph.D. Marco Congedo ID of the dataset: BI.EEG.2013-GIPSA

Keywords

Experiment, Brain-Computer Interface, Calibration, Electroencephalography (EEG), P300, Adaptive

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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