Neural decoding of continuous speech EEG signals

Interpretation of neural signals to a form that is as intelligible as speech facilitates the development of communication mediums for the otherwise speech/motor impaired individuals. Speech perception, production and imagination often constitute phases of human communication. The primary goal of our work is to analyze the similarity between these three phases by studying electroencephalogram(EEG) patterns across these modalities, in order to establish their usefulness for brain computer interfaces(BCI). Fundamental syllabic units of speech in these phases are decoded accurately using temporal modelling based machine learning approaches generalizing over multiple sentences, trials, sessions, and subjects. 1