Mental performance classification using fused multilevel feature generation with EEG signals

Abstract

Mental performance classification is a critical issue for brain-computer interfaces. Accurate and reliable classification of good or bad mental performance gives important clues for the preliminary diagnosis of some diseases and mental stress. In this work, we put forward an objective artificial intelligence model to quantify the clarity of thought during mental arithmetic tasks. The proposed model consists of: (i) multilevel feature extraction based on statistical and texture analysis methods, (ii) feature ranking and selection with a Chi2 method, (iii) classification, and (iv) weightless majority voting classifier. The novelty of the presented model comes from multilevel fused feature generation. The presented model was developed using 20 channel electroencephalography data from 36 subjects. The signals were captured while the subjects were performing mental arithmetic tasks. The individual datasets were labeled as either good or bad, based on the task results. We have obtained an accuracy of 96.77% using O2 channel with a k-nearest neighbor classifier and reached 100.0% accuracy with the majority voting classifier. Our results indicate that it is possible to determine mental performance with artificial intelligence. That might be a steppingstone to establish objective measures for the clarity of thought during a wide range of mental tasks.