  Classification of cognitive workload promises immense benefit in diverse
areas ranging from driver safety to augmenting human capability through closed
loop brain computer interface. The brain is the most metabolically active organ
in the body and increases its metabolic activity and thus oxygen consumption
with increasing cognitive demand. In this study, we explore the feasibility of
in-ear SpO2 cognitive workload tracking. To this end, we preform cognitive
workload assessment in 8 subjects, based on an N-back task, whereby the
subjects are asked to count and remember the number of odd numbers displayed on
a screen in 5 second windows. The 2 and 3-back tasks lead to either the lowest
median absolute SpO2 or largest median decrease in SpO2 in all of the subjects,
indicating a robust and measurable decrease in blood oxygen in response to
increased cognitive workload. Using features derived from in-ear pulse
oximetry, including SpO2, pulse rate and respiration rate, we were able to
classify the 4 N-back task categories, over 5 second epochs, with a mean
accuracy of 94.2%. Moreover, out of 21 total features, the 9 most important
features for classification accuracy were all SpO2 related features. The
findings suggest that in-ear SpO2 measurements provide valuable information for
classification of cognitive workload over short time windows, which together
with the small form factor promises a new avenue for real time cognitive
workload tracking.

Signal Processing