Spatial filtering and single-trial classification of EEG during vowel speech imagery

Abstract

With the purpose of providing assistive technology for the communication impaired, we propose a control algorithm for speech prostheses using vowel speech imagery. Electroen-cephalograms were recorded in three healthy subjects during the performance of three tasks, imaginary speech of the English vowels /a/ and /u/, and a no action state as control. Speech related potentials were visualized by grand averaging in the time domain. Feature data was obtained by filtering the time series data using optimal spatial filters designed through the common spatial patterns method. Resultant feature vectors were classified using a nonlinear support vector machine. Overall classification accuracies ranged from 68 to 78%. Results indicate significant potential for the use of vowel speech imagery as a speech prosthesis controller.