The Detection of Anomaly in Electroencephalogram with Deep Convolutional Neural Networks

In order to solve the problem of high error rate in EEG automatic detection, a method of EEG anomaly detection based on deep convolution neural network (CNN) is proposed. Firstly, multiple heterogeneous data sources are reconstructed and preprocessed according to the standard, and a training set with 118 716 samples and a test set with 12 022 samples are generated. Secondly, a deep CNN model with fast connection is constructed. Then, the model is tested and adjusted on the training set, and the best model parameters are saved. Finally, the model is predicted on the test set. The prediction results show that the model achieves 94.33% classification accuracy. EEG features can be automatically extracted and abnormal recognition can be carried out through the processing and analysis of EEG signals with the proposed method, so as to achieve the purpose of rapid detection and auxiliary diagnosis.