Abstract
In this work, an attempt has been made to discriminate arousal and valence dimensions in Electrodermal Activity (EDA) signals using Spiking Deep Belief Network (SDBN). EDA signals having different arousal and valence dimensions are obtained from public online database. These signals are divided into equal parts and normalized using channel normalization. Later, signals are subjected to SDBN for event-related features and classification. A leave-one-out cross validation is used to investigate the classification performance. The result shows that the SDBN classifiers are able to discriminate the emotional states. The network yields better classification performance for emotional dimensions of arousal and valence. It appears that the proposed approach can be used to differentiate autonomic and pathological conditions.
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