Brain Functional Connectivity Analysis and Crucial Channel Selection Using Channel-Wise CNN

Abstract

Brain functional connectivity analysis and crucial channel selection, play an important role in brain working principle exploration and EEG-based emotion recognition. Towards this purpose, a novel channel-wise convolution neural network (CWCNN) is proposed, where every group convolution operator is imposed only on a separate channel. The inputs and weights of the full connection layer are visualized by using the brain topographic maps to analyze brain functional connectivity and select the crucial channels. Experiments are carried out on the SJTU emotion EEG database (SEED). The results demonstrate that positive and neutral emotions evoke greater brain activities than negative emotions in the left frontal region, which is consistent with the result from the power spectrum analysis in the literature. Meanwhile, 16 crucial channels, which are mainly distributed in the frontal and temporal regions, are selected based on the proposed method to improve emotion recognition performance. The classification accuracy by using the selected crucial channels is similar to that without channel selection. But the model with the 16 selected channels is more memory-efficient and the computation time can be reduced substantially.

This work is supported in part by the National Natural Science Foundation of China (Grants 91648208 and 61720106012), Beijing Natural Science Foundation (Grants 3171001 and L172050).