Communication between cortices mediated by deep brain structures such as the amygdala and fusiform gyrus has been suggested to explain the enhanced perception of stimuli bearing emotional content or having facial features. In this paper, we analyze the dependence structure of the relevant brain regions to assess their connectivity in response to a facial stimulus, and to discriminate it from a mock stimulus. The proposed approach treats the brain as a graphical network where vertices correspond to locations of electroencephalogram (EEG) recordings, and weights of the edges correspond to dependence values. We employ a novel measure of dependence, called generalized measure of association (GMA), due to its underlying simplicity, and compare its performance against Pearson's correlation. The performance is assessed in terms of the discriminability between the face and mock stimuli. We observe that GMA successfully exhibits higher dependence in regions that might reflect the activity of the amygdaloid complex and the right fusiform gyrus when the stimulus is face. Furthermore, the distributions of the dependence values show that GMA also achieves a better separation between face and mock, compared to correlation.
|Title of host publication||Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||4|
|Publication status||Published - 25 Mar 2012|