Towards an Affective Brain-Computer Interface Monitoring Musical Engagement

A non-invasive way to monitor a music listener´s level of engagement could give us a valuable tool for music classification, technology, and therapy. To investigate whether musical engagement can be monitored, we developed an experimental protocol using the mobile brain/body imaging (MoBI) paradigm in which participants make expressive rhythmic arm gestures to encourage and/or index musical engagement. Participants communicate the feeling pulse of music they are hearing via simple rhythmic U-shaped back-and-forth hand/arm ´conducting´ gesture cycles that animate, in real time, the mirroring movement of a spot of light on a video display in front of them. Participants are asked to imagine that this display is also being viewed remotely by a deaf friend to whom they are attempting to communicate the feeling of the music they are hearing. In an Engaged condition, listeners are encouraged to fully engage themselves in this musical/emotional communication task. In a Not Engaged condition, a concurrent internal arithmetic distractor task is introduced to induce less fully engaged listening. Here, we report results of training a classifier using a frequency-based common spatial patterns (FBCSP) approach to correctly distinguish Engaged and Not Engaged conditions from concurrently recorded EEG data. Here the approach gave 67% classification accuracy across subjects (versus 50% chance), and 85% accuracy within subjects, cross-validated using a block wise paradigm.