Modeling Physiological Data with Deep Belief Networks.

Feature extraction is key in understanding and modeling of physiological data. Traditionally hand-crafted features are chosen based on expert knowledge and then used for classification or regression. To determine important features and pick the effective ones to handle a new task may be labor-intensive and time-consuming. Moreover, the manual process does not scale well with new or large-size tasks. In this work, we present a system based on Deep Belief Networks (DBNs) that can automatically extract features from raw physiological data of 4 channels in an unsupervised fashion and then build 3 classifiers to predict the levels of arousal, valance, and liking based on the learned features. The classification accuracies are 60.9%, 51.2%, and 68.4%, respectively, which are comparable with the results obtained by Gaussian Naïve Bayes classifier on the state-of-the-art expert designed features. These results suggest that DBNs can be applied to raw physiological data to effectively learn relevant features and predict emotions.