Reinforcement online learning for emotion prediction by using physiological signals

Physiological signals generated from human internal organs can objectively and truly reflect the real-time variations of human emotion and monitor body situation. Recently, with the accessibility of a massive number of physiological signal data, emotion analysis by using physiological signals is attracting an increasing attention and many methods have been reported by using electroencephalogram (EEG) or peripheral physiological signals. Although the prominent online learning methods can predict the emotion status with time varying physiological signals, it does not consider the reward of current operation in each iteration. To tackle this problem, in this paper, we propose a reinforcement online learning (ROL) method for real-time emotion state prediction by exploiting the reward to modify the predictor during the online training iterations. In each iteration, we evaluate the reward and then select some specific instances into predictor learning. It gains both significant time reduction and prominent performance. We apply the reinforcement online learning to least squares (LS) and support vector regression (SVR) for Emotion Prediction, respectively. Extensive experiments are conducted on artificial dataset and real-world physiological signal dataset (DEAP dataset) and the experimental results validate the effectiveness of the proposed method.