EEG Analysis of Imagined Speech

Abstract

Scalp electroencephalogram (EEG) is one of the most commonly used methods to acquire EEG data for brain-computer interfaces (BCIs). Worldwide a large number of people suffer from disabilities which impair normal communication. Communication BCIs are an excellent tool which helps the affected patients communicate with others. In this paper scalp EEG data is analysed to discriminate between the imagined vowel sounds /a/, /u/ and no action or rest as control state. Mean absolute deviation (MAD) and Arithmetic mean are used as features to classify data into one of the classes /a/, /u/ or rest. With high classification accuracies of 87.5-100% for two class problem and 78.33-96.67% for three class problem that have been obtained in this work, this algorithm can be used in communication BCIs, to develop speech prosthesis and in synthetic telepathy systems.