Detection of Highly Motivated Time Segments in Brain Computer Interface Signals

Abstract

ABSTRACTMotivation of a subject, who is associated with the data acquisition of brain computer interface (BCI) experiment, is a very crucial parameter for executing a successful BCI application. This paper proposes a novel method to present the distribution of motivation of a subject during a BCI experiment. The proposed method was successfully applied to the BCI Competition 2003 Data Set III and the BCI Competition 2005 Data Set I using fast Fourier transform-based band power features with a linear discriminant analysis classifier. The results show that not only the motivation of the subject dramatically changes during the trial but also using highly motivated time segments provides 7.86% and 2.00% improvement in the classification accuracy of the BCI Competition 2003 Data Set III and the BCI Competition 2005 Data Set I, respectively.