Analyzing Brain Dynamics of Affective Engagement

Development of EEG-based brain computer interface (BCI) methods has largely focused on creating a communication channel for subjects with intact cognition but profound loss of motor control from stroke or neurodegenerative disease, allowing such subjects to communicate by spelling out words on a personal computer. However, another important human communication channel may also be limited or unavailable in handicapped subjects -direct non-linguistic emotional communication as by gesture, vocal prosody, and facial expression. We report and examine a first demonstration of an ‘emotion BCI’ in which, as one element of a live musical performance, an able-bodied subject successfully engaged the electronic delivery of an ordered sequence of five music two-tone ground intervals by imaginatively reexperiencing the human feeling he had spontaneously associated with the sound of each interval during training sessions. The EEG data included activities of both brain and non-brain sources (scalp muscles, eye movements). Common Spatial Pattern classification gave 84% correct pseudo-online performance and 5-of-5 correct classification in live performance. Re-analysis of the training session data including only brain EEG sources found by multiple-mixture Amica ICA decomposition achieved five-class classification accuracy of 5970%, confirming that different imagined emotion states may be associated with distinguishable brain source EEG dynamics. [email protected]