Method of Data Dimensionality Reduction in Brain-Computer Interface Systems

Abstract

The article is devoted to the problems of performance increasing of information-measuring and control systems based on brain-computer interface technology (BCI). BCI is a technology that allows communication between the brain and the external environment only on the basis of processing of the electroencephalogram (EEG). The functioning of the BCI system can be represented as a cycle. In each iteration, the EEG signal is measured and preprocessed, the characteristic features are extracted, the classification is implemented and the control action corresponding to the recognized command of the operator is generated. For the functioning of BCI systems in real-time mode it is necessary to solve the problem of the processed data dimensionality reduction (without losing significant information). The article describes the author's algorithm which is designed to use for that purpose. The algorithm is based on the use of digital signal processing and cluster analysis. Also, the results of experimental testing of the approach are described in the article. The experiments showed that proposed approach allows to significantly reduce the time required to perform operations of data dimensionality reduction. In addition, it’s using has not negative affect on the clustering quality of processed sets of signals. It is experimentally confirmed that the developed algorithm effectively works in conjunction with the linear discriminant analysis (LDA), acting as a preprocessor for the LDA. At the same time, the speed of such bundle is much higher than speed of LDA without the preprocessor.