Deep belief networks for electroencephalography: A review of recent contributions and future outlooks.
Capturing user’s emotional state is an emerging way for implicit relevance feedback in information retrieval (IR). Recently, EEGbased emotion recognition has drawn increasing attention. However, a key challenge is effective learning of useful features from EEG signals. In this paper, we present our on-going work on using Deep Belief Network (DBN) to automatically extract highlevel features from raw EEG signals. Our preliminary experiment on the DEAP dataset shows that the learned features perform comparably to the use of manually generated features for emotion recognition.