Convolutional Neural Network applied in EEG imagined phoneme recognition system

Speech is a skill that most of the time we take for granted. In reality, this ability is a complex mechanism which requires thoughts to be translated into words who are further transposed into sounds, a mechanism which involves precise coordination of several muscles and joints. In some cases, this complex mechanism can no longer be performed and may be accompanied by almost complete loss of motor activity such in diseases as: stroke, Lock-Down syndrome, amyotrophic lateral sclerosis, cerebral palsy etc. The most recent method that aims to supplement the speech mechanism is imaginary speech recognition using electroencephalographic (EEG) signals, by using complex computing mechanisms like Deep Learning (DL) in order to decode the thoughts. In this paper we aim to recognize three types of clustered phonemes using conventional speech recognition techniques, like Mel-Cepstral Coefficients (MFCC) and Linear Predictive Coding (LPC) combined with Convolutional Neural Networks (CNN). We compared four types of features extraction: MFCC, LPC, MFCC+ LPC combined into 1-channel matrix and MFCC+ LPC combined into a 2-channel matrix. We showed that MFCC coefficients offer a better accuracy than LPC and that concatenating MFCC and LPC into a 2-channel matrix we obtain a better performance than combining them into 1-channel matrix.