Bubble

Neuroscience

Support vector machine

Classifier (UML)

Parallel computing

Computer science

Maximum bubble pressure method

Brain–computer interface

Psychology

Pattern recognition (psychology)

Interface (matter)

Artificial intelligence

Electroencephalography

Machine learning

Speech recognition

IEEE Transactions on Biomedical Engineering

Brain-computer interface P300 speller aims at helping patients unable to activate muscles to spell words by means of their brain signal activities. Associated to this BCI paradigm, there is the problem of classifying electroencephalogram signals related to responses to some visual stimuli. This paper addresses the problem of signal responses variability within a single subject in such brain-computer interface. We propose a method that copes with such variabilities through an ensemble of classifiers approach. Each classifier is composed of a linear support vector machine trained on a small part of the available data and for which a channel selection procedure has been performed. Performances of our algorithm have been evaluated on dataset II of the BCI Competition III and has yielded the best performance of the competition.

BCI Competition III: Dataset II- Ensemble of SVMs for BCI P300 Speller