EEG feature selection for thought driven robots using evolutionary Algorithms

Abstract

Machine control using electroencephalography (EEG) based brain computer interfaces (BCI) has been extensively researched in the past decade. However, research is often based on event bound methods such as motor imagery. Despite being useful in medical applications, even bound methods limit users' operational capability while performing BCI control. To alleviate the said limitation, we explore a robot control framework based on abstract thought. Abstract thought in this context is defined as conscious mental tasks that are not bound with any particular event or bodily movement. This paper presents an initial step in the framework, which is a methodology for optimal feature selection for abstract thought EEG data classification. The presented method contains 2 steps: 1) generational Genetic Algorithm (GA) based feature selection, and, 2) EEG data classification using selected features. The presented method was implemented on an EEG dataset acquired from a consumer grade EEG device. Abstract thought EEG data were collected for three actions pertaining to robot control; 1) “rest”, 2) “move forward”, and, 3) “turn left”. The presented method was compared to EEG classification without any feature selection. Experimental results showed that the presented method outperformed the method without feature selection for all the tested classifiers with a 10% or higher improvement in classification accuracy.