Detection and classification of pilots cognitive state using EEG

Electroencephalogram (EEG) data is a set of brain signals recorded by special EEG headsets. These signals reflect the cortical electrical activity. The technique for utilization of EEG data has emerged to be a safe and portable non-invasive Brain Computer Interface (BCI) that can easily be used for studying the human cognitive states. In this paper we have focused on studying the pilot's cortical potentials in simulated flight environment in order to classify his mental state into three categories i.e. rest mode, navigation flying mode, and dogfight mode. 14 channel Emotiv EEG headset was used by the subjects while playing a fighter aircraft game which could simulate all the required scenarios. The subject was screened in a dark room with huge projector screen along with audio stimuli. Several sessions of EEG data were recorded and feature extraction was carried out. Random Forest Tree classification algorithm proved to produce the best results. The pilot's cognitive state was classified according to the labeled recordings by taking one second of data each time and classifying it. As a result 81.7 % accuracy was achieved. The decent accuracy of results prove that real time pilot cognitive state can be decoded effectively, and if transmitted live onto the ground command and control room, it can be utilized for ensuring pilots safety as well as for training and monitoring of pilots on-board activities.