This paper addresses the problem of signal responses variability within a single subject in P300 speller Brain-Computer Interfaces.

Oct 22, 2024 · This paper addresses the problem of signal responses variability within a single subject in P300 speller Brain-Computer Interfaces.

Abstract. This paper addresses the problem of signal responses vari- ability within a single subject in P300 speller Brain-Computer Interfaces.

This paper shows that noise can improve the accuracy of brain-computer interface (BCI) systems. Additive Gaussian noise can benefit arrays of ensemble support ...

Alain Rakotomamonjy, Vincent Guigue, Grégory Mallet, Victor Alvarado: Ensemble of SVMs for Improving Brain Computer Interface P300 Speller Performances.

Brain-Computer Interface P300 speller aims at helping patients unable to activate muscles to spell words by means of their brain signal activities.

Results: The SVM ensemble with extended training set achieved 85% classification accuracy for the averaged results of four sequences, and 100% for 11 sequences ...

—Brain–computer interface P300 speller aims at helping patients unable to activate muscles to spell words by means of their brain signal activities.

Support vector machine (SVM) ensemble has been successfully applied to classify in the P300-speller. However, large scale of training data was needed for the ...

The detection of the presence of the P300 in the electroencephalogram (EEG) is a challenging issue in P300-based brain–computer interface (BCI).