  In this paper, electroencephalography (EEG) measurements are used to infer
change in cortical functional connectivity in response to change in audio
stimulus. Experiments are conducted wherein the EEG activity of human subjects
is recorded as they listen to audio sequences whose quality varies with time. A
causal information theoretic framework is then proposed to measure the
information flow between EEG sensors appropriately grouped into different
regions of interest (ROI) over the cortex. A new causal bidirectional
information (CBI) measure is defined as an improvement over standard directed
information measures for the purposes of identifying connectivity between ROIs
in a generalized cortical network setting. CBI can be intuitively interpreted
as a causal bidirectional modification of directed information, and inherently
calculates the divergence of the observed data from a multiple access channel
with feedback. Further, we determine the analytical relationship between the
different causal measures and compare how well they are able to distinguish
between the perceived audio quality. The connectivity results inferred indicate
a significant change in the rate of information flow between ROIs as the
subjects listen to different audio qualities, with CBI being the best in
discriminating between the perceived audio quality, compared to using standard
directed information measures.
