Hybrid EEG-fNIRS based quadcopter control using active prefrontal commands

Abstract

In this paper, we have improved the classification accuracy of four prefrontal commands decoded using hybrid electroencephalography-functional near-infrared spectroscopy (EEG-fNIRS) for quadcopter. Mental arithmetic, mental counting, word formation, and mental rotation are used as brain task to generate the commands. The brain signals are decoded simultaneously in a single window using hybrid EEG-fNIRS. We extracted the neuronal and hemodynamic features in 0∼2 sec, 0∼2.25 sec, and 0∼2.5 sec windows. An overlapping window of 0.25 sec is used for online/real-time analysis. Signal peak, signal mean, and signal power are computed as features for EEG. Signal mean, signal slope, signal peak, and minimum negative value are computed as features for fNIRS. We used linear discriminant analysis to classify the features in online scenario. The generated commands are transferred to a quadcopter using Wi-Fi. The quadcopter movements are controlled by the transmitted brain commands. Our results showed that overall system accuracy for fNIRS was increased from 69% to 84 % by combining features with EEG. This enabled more stable control for the quadcopter. Therefore the result seems significant for brain-computer interface applications.