Emotion recognition from biometrics is relevant to a wide range of
application domains, including healthcare. Existing approaches usually adopt
multi-electrodes sensors that could be expensive or uncomfortable to be used in
real-life situations. In this study, we investigate whether we can reliably
recognize high vs. low emotional valence and arousal by relying on noninvasive
low cost EEG, EMG, and GSR sensors. We report the results of an empirical study
involving 19 subjects. We achieve state-of-the- art classification performance
for both valence and arousal even in a cross-subject classification setting,
which eliminates the need for individual training and tuning of classification
models.Comment: To appear in Proceedings of ACII 2017, the Seventh International
Conference on Affective Computing and Intelligent Interaction, San Antonio,
TX, USA, Oct. 23-26, 201
