Algorithm Research Based on Multi-Feature Fusion of EEG Signals with Convolutional Neural Networks

In order to address the issue of low classification accuracy in motor imagery of electroencephalogram (EEG) signals, a feature extraction algorithm based on sample entropy and common spatial pattern (CSP) feature fusion has been proposed. The algorithm initially performs wavelet packet decomposition on the raw EEG signal, selecting the components containing μ and β rhythms for reconstruction. Subsequently, the sample entropy and CSP features of the reconstructed signal are separately extracted. These two features are then fused to create a new feature vector which is recognized using a one-dimensional convolutional neural network designs in the paper, to obtain the classification result. The proposes method achieves a classification accuracy of 91.66% on the BCI Dataset III in 2003 and an average classification accuracy of 85.29% on the BCI Dataset A in 2008. Comparing with multi-feature fusion algorithms proposed in recent literature, the accuracy is improved by 7.96 percentage points. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.