EEG Recognition with Adaptive Noise Reduction Based on Convolutional LSTM Network

Abstract

In this paper, a new EMD adaptive decomposition algorithm is designed to denoise the original EEG signals, and a deep neural network model ConvLSTM is used to extract the features of the denoised signals. First, EEG signals are collected by a brain equipment. Then we use the proposed method to denoise the collected signals. Finally, the needed features are extracted with the convLSTM. Compared with previous methods, this proposed algorithm can extract the temporal and spatial characteristics of EEG more effectively. The proposed method is implemented on the actual moving EEG dataset, which verifies the validity and practicability of the proposed model.