Efficient classification system based on Fuzzy–Rough Feature Selection and Multitree Genetic Programming for intension pattern recognition using brain signal

•We design a classification system for brain signal based on evolutionary algorithm.•The proposed methods base on fuzzy rough theory and Multitree Genetic Programming.•We examine the intension pattern of brain signals with fNIRS.•The proposed FRFS reduced the data volume and extracted the informative features.•The proposed GP classified with higher accuracy than conventional methods. Recently, many researchers have studied in engineering approach to brain activity pattern of conceptual activities of the brain. In this paper we proposed a intension recognition framework (i.e. classification system) for high accuracy which is based on Fuzzy–Rough Feature Selection and Multitree Genetic Programming. The enormous brain signal data measured by fNIRS are reduced by proposed feature selection and extracted the informative features. Also, proposed Multitree Genetic Programming use the remain data to construct the intension recognition model effectively. The performance of proposed classification system is demonstrated and compared with existing classifiers and unreduced dataset. Experimental results show that classification accuracy increases while number of features decreases in proposed system.