Analysis of EEG Signals Fused With CNN and Complex Networks

Abstract

Currently, brain-computer interface technology still poses hidden dangers in the complete control of ideas. Additionally, there are issues with the low sampling frequency and accuracy of EEG signal acquisition equipment. To address these concerns, this study proposes a combined model for EEG signal recognition and classification analysis by combining frequency division multi-feature complex brain networks with parallel convolutional neural networks. The effectiveness of this model has been verified. In model visualization analysis, the visualization results of t-distribution random neighborhood embedding in the third separable convolutional layer indicate that the two types of imagination have already experienced separation. There is a clear boundary between the two at position 0 on both the horizontal and vertical axes. This is a significant improvement compared to the comparative model. In the model performance verification, the full band classification accuracy in the synchronous network was maintained between 60% to 84%, and the μ-rhythm was maintained between 59% to 81%. The average classification accuracy of the combined model was 77.40% with higher performance, which was higher than 68.53% and 70.87% of the single scale convolutional neural network. In comparison with deep learning algorithms, the average classification accuracy of the combined model was 85.74%, much higher than the 66.20% and 76.69% of the comparative models. The composite model constructed has good performance in recognizing and classifying electroencephalogram signals. It can be effectively applied in practical brain-computer interface technology or electroencephalogram signal analysis.