Brain Invaders Adaptive versus Non-Adaptive P300 Brain-Computer Interface dataset
This provides a dataset for researchers in BCI and neuroscience to study adaptive calibration methods, but it is incremental as it focuses on data collection rather than new findings.
The authors tackled the problem of comparing adaptive versus non-adaptive calibration in a P300 brain-computer interface (BCI) by creating and publicly releasing a dataset of EEG recordings from 24 subjects, with no specific results or numbers reported beyond the dataset availability.
We describe the experimental procedures for a dataset that we have made publicly available at https://doi.org/10.5281/zenodo.1494163 in mat and csv formats. 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. 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.