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Chen, Yongqiang; Zhang, Wenya; Song, Dawei; Zhang, Peng; Hou, Yuexian and Ren, Qingtao
(2015).
URL: https://docs.google.com/viewer?a=v&pid=sites&srcid...
Abstract
Capturing user’s emotional state is an emerging way for implicit relevance feedback in information retrieval (IR). Recently, EEG-based 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 high-level 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.