A modified grey wolf optimization based feature selection method from EEG for silent speech classification

摘要

Brain computer interfaces (BCI's) employing electroencephalographic signals are being applied to a wide variety of applications like motor imagery task classification, prosthetics etc. Electroencephalography (EEG) data are inherently non-stationary and noisy, and as such identification of appropriate features for classification is a crucial task. Selection of features based on genetic algorithms (GA) has been applied, but it leads to a redundant set of features. In the present work, grey wolf optimization (GWO) based feature selection method has been applied on EEG data for silent speech classification. The EEG data from the ABISSR (Analysis of Brain Waves and development of intelligent model for silent speech recognition) project was used in the proposed work. An accuracy of 65% was obtained in classifying five imagined vowels /a/, /e/, /i/, /o/ and /u/ from EEG data using support vector machine (SVM). Moreover, it was observed that the GWO outperformed GA in optimization.