Feature Extraction Method of EEG Signals Evaluating Spatial Cognition of Community Elderly With Permutation Conditional Mutual Information Common Space Model

In order to improve the traditional common space pattern (CSP) algorithm pattern in EEG feature extraction, this study proposes a feature extraction method of EEG signals based on permutation conditional mutual information common space pattern (PCMICSP), which used the sum of the permutation condition mutual information matrices of each lead to replacing the mixed spatial covariance matrix in the traditional CSP algorithm, and its eigenvectors and eigenvalues are used to construct a new spatial filter. Then the spatial features in the different time domains and frequency domains are combined to construct the two-dimensional pixel map, Finally, a convolutional neural network (CNN) is used for binary classification. The EEG signals of 7 community elderly before and after spatial cognitive training in virtual reality (VR) scenes were used as the test data set. The average classification accuracy of the PCMICSP algorithm for pre-test and post-test EEG signals is 98%, which was higher than that of CSP based on CMI (conditional mutual information), CSP based on MI (mutual information), and traditional CSP in the combination of four frequency bands. Compared with the traditional CSP method, PCMICSP can be used as a more effective method to extract the spatial features of EEG signals. Therefore, this paper provides a new approach to solving the strict linear hypothesis of CSP and can be used as a valuable biomarker for the spatial cognitive evaluation of the elderly in the community.