Analysis of motor imaginary BCI within multi-environment scenarios using a mixture of classifiers

Abstract

Brain-computer interface (BCI) is a system that provides communication between human beings and machines through an analysis of human brain neural activity. Several studies on BCI systems have been carried out in controlled environments, however, a functional BCI should be able to achieve an adequate performance in real environments. This paper presents a comparative study on alternative classification options to analyze motor imaginary BCI within multi-environment real scenarios based on mixtures of classifiers. The proposed methodology is as follows: The imaginary movement detection is carried out by means of feature extraction and classification, in the first stage; feature set is obtained from wavelet transform, empirical mode decomposition, entropy, variance and rates between minimum and maximum, in the second stage, where several classifier combinations are applied. The system is validated using a database, which was constructed using the Emotiv Epoc+ with 14 channels of electroencephalography (EEG) signals. These were acquired from three subject in 3 different environments with the presence and absence of disturbances. According to the different effects of the disturbances analyzed in the three environments, the performance of the mixture of classifiers presented better results when compared to the individual classifiers, making it possible to provide guidelines for choosing the appropriate classification algorithm to incorporate into a BCI system.