Emotional responses as independent components in EEG

With neuroimaging studies showing promising results for discrimination of affective responses, the perspectives of applying these to create more personalised interfaces that adapt to our preferences in real-time seems within reach. Additionally the emergence of wireless electroencephalograph (EEG) neuroheadsets and smartphone brainscanners widens the possibilities for this to be used in mobile settings on a consumer level. However the neural signatures of emotional responses are characterized by small voltage changes that would be highly susceptible to noise if captured in a mobile context. Hypothesizing that retrieval of emotional responses in mobile usage scenarios could be enhanced through spatial filtering, we compare a standard EEG electrode-based analysis against an approach based on independent component analysis (ICA). By clustering scalp maps and time series responses we identify neural signatures that are differentially modulated when passively viewing neutral, pleasant and unpleasant images. While early responses can be detected from the raw EEG signal, we identify multiple early and late ICA components that are modulated by emotional content. We propose that similar approaches to spatial filtering might allow us to retrieve more robust signals in real-life mobile usage scenarios, and potentially facilitate design of cognitive interfaces that adapt the selection of media to our emotional responses.