Deep Convolution Neural Network and Autoencoders-Based Unsupervised Feature Learning of EEG Signals

Epilepsy is a health problem that seriously affects the quality of humans for many years. Therefore, it is important to accurately analyze and recognize epilepsy based on EEG signals, and for a long time, researchers have attempted to extract new features from the signals for epilepsy recognition. However, it is very difficult to select useful features from a large number of them in this diagnostic application. As the development of artificial intelligence progresses, unsupervised feature learning based on the deep learning model can obtain features that can better describe identified objects from unlabeled data. In this paper, the deep convolution network and autoencoders-based model, named as AE-CDNN, is constructed in order to perform unsupervised feature learning from EEG in epilepsy. We extract features by AE-CDNN model and classify the features based on two public EEG data sets. Experimental results showed that the classification results of features obtained by AE-CDNN are more optimal than features obtained by principal component analysis and sparse random projection. Using several common classifiers to classify features obtained by AE-CDNN model results in high accuracy and not inferior to the research results from most recent studies. The results also showed that the features of AE-CDNN model are clear, effective, and easy to learn. These features can speed up the convergence and reduce the training times of classifiers. Therefore, the AE-CDNN model can be effectively applied to feature extraction of EEG in epilepsy.