Silent Speech Recognition using EEG Signals *

Patients suffering from paralysis, and neuro-muscular diseases are unable to communicate. Hence, there is a need for an alternative way of communication. This research work has tried to address this issue using Electroencephalograph (EEG) signals. EEG is the recording of electrical activity produced by the firing of neurons within the brain. However, EEG recordings are always contaminated with artifacts, which hinder the decoding process. Therefore, identifying and removing artifacts is an important step. For this, a fresh EEG dataset with six words is collected from 10 subjects. The artifacts which contaminate the quality of EEG data are removed and empirical mode decomposition is used to decompose EEG signals into various intrinsic mode functions. Linear and nonlinear timedomain features are extracted from the modes. A feature set is obtained by selecting highly discriminant features using the analysis of variance test. Classification is performed using seven recent machine learning algorithms.