Audio-video emotional response mapping based upon Electrodermal Activity

In this paper, a machine learning algorithm is proposed for emotional pattern recognition during audiovisual stimuli (music videos) using Electrodermal Activity ( (B)EDA). For emotion prediction apart from conventional time domain features of EDA signal, various features in different signal representation i.e. frequency and wavelet were analysed. The comparative result indicated that the wavelet features subset outperformed the conventional time domain features in term of classification accuracy. For identification of optimal network configuration, various combination of optimization algorithms (i.e. backpropagation algorithms) and error function were explored. The best performance of 79% for arousal, 69.8% for valence and 71.2% for dominance were obtained for emotion recognition respectively.