Emotional responses as independent components in EEG

Combine wireless neuroheadsets with smartphones that enable mobile brain imaging can potentially allow us to design cognitive interfaces which adapt to our affective responses. Neuroimaging experiments using electroencephalography (EEG) initially identified two components elicited by pleasant or unpleasant images; early posterior negativity (EPN) and late positive potential (LPP). Recent studies suggest that several time course components may be modulated by emotional content in images or text. However these neural signatures 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.