In-Ear SpO2 for Classification of Cognitive Workload

TL;DR

This study demonstrates that in-ear SpO2 measurements can accurately classify cognitive workload levels in real-time, offering a compact and effective method for monitoring mental effort using pulse oximetry data.

Contribution

It introduces a novel approach using in-ear pulse oximetry features, especially SpO2, for classifying cognitive workload with high accuracy in a practical setting.

Findings

In-ear SpO2 decreases with increased cognitive load.

Achieved 94.2% accuracy in classifying N-back tasks.

SpO2 features are the most important for workload classification.

Abstract

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.