Implicit emotion communication: EEG classification and haptic feedback

Today, ubiquitous digital communication systems do not have an intuitive, natural way of communicating emotion, which, in turn, affects the degree to which humans can emotionally connect and interact with one another. To address this problem, a more natural, intuitive, and implicit emotion communication system was designed and created that employs asymmetry-based EEG emotion classification for detecting the emotional state of the sender and haptic feedback (in the form of tactile gestures) for displaying emotions for a receiver. Emotions are modeled in terms of valence (positive/negative emotions) and arousal (intensity of the emotion). Performance analysis shows that the proposed EEG subject-dependent emotion classification model with Free Asymmetry features allows for more flexible feature-generation schemes than other existing algorithms and attains an average accuracy of 92.5% for valence and 96.5% for arousal, outperforming previous-generation schemes in high feature space. As for the haptic feedback, a tactile gesture authoring tool and a haptic jacket were developed to design tactile gestures that can intensify emotional reactions in terms of valence and arousal. Experimental study demonstrated that subject-independent emotion transmission through tactile gestures is effective for the arousal dimension of an emotion but is less effective for valence. Consistency in subject-dependent responses for both valence and arousal suggests that personalized tactile gestures would be more effective.