The EEG Signal Analysis for Spatial Cognitive Ability Evaluation Based on Multivariate Permutation Conditional Mutual Information-Multi-Spectral Image.

This study aims to find an effective method to evaluate the efficacy of cognitive training of spatial memory under a virtual reality environment, by classifying the EEG signals of subjects in the early and late stages of spatial cognitive training. This study proposes a new EEG signal analysis method based on Multivariate Permutation Conditional Mutual Information-Multi-Spectral Image (MPCMIMSI). This method mainly considers the relationship between the coupled features of EEG signals in different channel pairs and transforms the multivariate permutation conditional mutual information features into multi-spectral images. Then, a convolutional neural networks (CNN) model classifies the resultant image data into different stages of cognitive training to objectively assess the efficacy of the training. Compared to the multi-spectral image transformation method based on Granger causality analysis (GCA) and permutation conditional mutual information (PCMI), the MPCMIMSI led to better classification performance, which can be as high as 95% accuracy. More specifically, the Theta-Beta2-Gamma-band combination has the best accuracy. The proposed MPCMIMSI method outperforms the multi-spectral image transformation methods based on GCA and PCMI in terms of classification performance. The MPCMIMSI feature in the Theta-Beta2-Gamma band is an effective biomarker for assessing the efficacy of spatial memory training. The proposed EEG feature-extraction method based on MPCMIMSI offers a new window to characterize spatial information of the noninvasive EEG recordings and might apply to assessing other brain functions.