Usability of EEG cortical currents in classification of vowel speech imagery

Abstract

With the purpose of providing assistive technology for the communication impaired, we propose a new approach for speech prostheses using vowel speech imagery. Using a hierarchical Bayesian method, electroencephalography (EEG) cortical currents were estimated using EEG signals recorded from three healthy subjects during the performance of three tasks, imaginary speech of vowels /a/ and /u/, and a no imagery state as control. The 3-task classification using a sparse logistic regression method with variational approximation (SLR-VAR) revealed that mean classification accuracy of cortical currents was almost two times greater than chance level and significantly higher than that using EEG signals. The results suggest the possibility of using EEG cortical currents to discriminate multiple syllables by improving the spatial discrimination of EEG.