EEG Signal Denoising Based on Deep Residual Shrinkage Network

In recent years, EEG signals are usually denoised by traditional algorithms, which suffer from large amounts of computation, a limited number of channels, or modal aliasing. To achieve better denoising, a deep residual contraction network is proposed to denoise EEG signals. At the same time, on the basis of the original residual shrinkage building units, a new soft threshold module is used to replace the ReLU function to construct a new network model. Through different groups of denoising experiments, the effectiveness of this denoising algorithm is verified.