< Back to previous page

Project

Cognitive workload monitoring during virtual reality gameplay by combining performance and task difficulty with EEG with ECG recordings

Current evidence suggests that short training sessions can improve cognitive performance of elder individuals and postpone age-related cognitive decline development. But the expected outcome could be compromised when these sessions increase the subject's cognitive workload (CW) -the interaction between mental demands of the task and the subject's ability to perform them. Several attempts have been made to relate CW to task difficulty, leaving out the subject-dependent factor. In order to gauge the latter, we propose to record EEGs, and to extract the EEG components that reflect attention-related and working memory processes, and electrocardiograms (ECGs) as the latter are known to be sensitive to CW, not only in terms of engagement, but also in terms of fatigue, drowsiness and boredom. We will consider a virtual reality (VR) game task as such settings have been shown to yield beneficial training effects. Our goal is to develop a statistical model that will combine VR game performance, task difficulty, and EEG and ECG recordings with the aim to predict and track, in real-time, an individual's CW during task performance and to adjust task complexity so as to avoid boredom and fatigue. As a proof of concept, we will test our approach on both healthy young and older individuals as CW differences are expected between these two age groups. When successful, it would signify a breakthrough in the applicability of EEG/ECG-based CW monitoring in cognitive training applications.

Date:24 Feb 2021 →  Today
Keywords:cognitive workload, EEG, event-related potential, spectral analysis, effectivity of cognitive training, virtual reality, working memory, N-Back task, real-time adaptive training, cognitive decline in elderly
Disciplines:Biomedical signal processing, Behavioural neuroscience, Cognitive neuroscience, Neurophysiology
Project type:PhD project