EEG Based Emotion Identification Using Unsupervised Deep Feature Learning


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.

Extracted Key Phrases

3 Figures and Tables

Citations per Year

Citation Velocity: 9

Averaging 9 citations per year over the last 3 years.

Learn more about how we calculate this metric in our FAQ.

Cite this paper

@inproceedings{Li2015EEGBE, title={EEG Based Emotion Identification Using Unsupervised Deep Feature Learning}, author={Xiang Li and Peng Zhang and Dawei Song and Guangliang Yu and Yuexian Hou and Bin Hu}, year={2015} }