Unsupervised EEG feature extraction based on echo state network

Advanced analytics such as event detection, pattern recognition, clustering, and classification with electroencephalogram (EEG) data often rely on extracted EEG features. Most of the existing EEG feature extraction approaches are hand-designed with expert knowledge or prior assumptions, which may lead to inferior analytical performances. In this paper, we develop a fully data-driven EEG feature extraction method by applying recurrent autoencoders on multivariate EEG signals. We use an Echo State Network (ESN) to encode EEG signals to EEG features, and then decode them to recover the original EEG signals. Therefore, we name our method feature extraction based on echo state network, or simply FE-ESN. We show that the well-known autoregression-based EEG feature extraction can be seen as a simplified variation of our FE-ESN method. We have conducted experiments on real-world EEG data to evaluate the effectiveness of FE-ESN for both classification tasks and clustering tasks. Experimental results demonstrate the superiority of FE-ESN over the state-of-the-art methods. This paper not only provides a novel EEG feature extraction method but also opens up a new way towards unsupervised EEG feature design.