An event related potential electroencephalogram signal analysis method based on denoising auto-encoder neural network

An algorithm based on denoising autoencoder neural network for event related potential analysis was proposed. Firstly, we establish a three layer neural network structure which is initialized by the denoising autoencoder. By using the unsupervised learning, the denoising autoencoder deep learning model is implemented. The weights obtained from the optimizing model are used as initialization parameters of the neural network by automatically learning of data characteristics from unlabeled data. Secondly, the training of the neural network can be completed through the fine-tuning of the network parameters with labeled data. This method effectively solves the problem of easy falling into local minimum for the neural network, which may be caused by random initialization. Thirdly, the proposed neural network was used in the competition III Data set II for classification analysis. Experimental results show that by using the denoising autoencoder neural network model under the training iterative of 2500, the average accuracy of 73.4%, 87.4% and 97.2% were obtained between subject A and subject B in three conditions which are the data is superimposed for 5, 10 and 15 times respectively. This significant results show that our framework demonstrated superior performance in the higher classification than other methods (97.2% in comparison the highest accuracy 96.5%). In summary, we provided a denoising autoencoder neural network, which can learn more robust features from training data automatically. This deep learning model would be a new method for event-related potential electroencephalogram (EEG) signals analysis. © 2019, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.