EEG feature learning with Intrinsic Plasticity based Deep Echo State Network

In this paper, deep EEG feature learning method is proposed for emotion recognition. It is well known that EEG signals dramatically vary from person to person, thereby making subject-independent emotion recognition very challenging. To address the above challenge, this work presents a deep echo state network (DeepESN) to learn temporal representation from raw EEG data. DeepESN as an input-driven discrete time non-linear dynamical system allows to process the temporal information at each time step in a deep temporal fashion by means of a hierarchical composition of multiple levels of recurrent neurons. To make the DeepESN robust, we pre-train the reservoir connections with an unsupervised intrinsic plasticity rule to generate activities following a desired Gaussian distribution. Then, we propose a hybrid learning algorithm for training the output weights which benefits from both the ridge regression and the online delta rule. Our leaky DeepESN achieved encouraging results when tested on the well-known affective benchmarks DEAP and DREAMER.