Increasing the accuracy of classification of brain signals based on lower limb motor imagery by combining optimal channel selection methods and common spatial pattern

  The brain-computer interface system provides a communication path between the brain and the computer and has recently received increasing attention. One of the most common paradigms of brain-computer interface systems is motor imagery. The braincomputer interface system based on motor imagery using electroencephalogram signals uses a large number of channels when receiving signals. Channels not related to the intended task cause unwanted interference and increase the noise level. In this paper, we present two optimal channel selection methods to improve common spatial pattern (CSP)-related features for classification of motor imagery tasks. Since the brain activities of motor imagery are located in a specific area of the brain, how to choose the right channels is important to improve the performance of the brain-computer interface. In this paper, analysis of variance (ANOVA) feature selection and sequential forward feature selection (SFFS) combined with common spatial pattern (CSP) are used to select optimal electrode channels. The results show that the accuracy of KNN, SVM and LDA classifications when using ANOVA+CSP method is 74, 72 and 71% respectively, when using SFFS+CSP method is 74, 73 and 68% respectively, when using CSP alone, 65, 62 and 60%, respectively, when not using the methods of selecting optimal channels and common spatial pattern, it is 58, 64 and 57%, respectively; Therefore, the combination of optimal channel selection methods and common spatial pattern has increased the accuracy of the classifiers.