OPTIMIZING MENTAL WORKLOAD ESTIMATION BY DETECTING BASELINE STATE USING VECTOR PHASE ANALYSIS APPROACH

• Main Author: LAM GHAI, LIM
• Format: Thesis
• Language: English
• Published: 2021
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://utpedia.utp.edu.my/id/eprint/24722/1/Lim%20Lam%20Ghai_13854.pdf; http://utpedia.utp.edu.my/id/eprint/24722/

Non-invasive brain imaging techniques offer an objective measure of mental workload by tapping directly into cognitive function. Among them, functional near-infrared spectroscopy (fNIRS) is an emerging technique that measures the hemodynamic response (HR). However, improper baseline return from the previous task-evoked HR contributes to a large variation in the subsequent HR, which affects the mental workload estimation. In this study, we propose a method using vector phase analysis to detect the baseline state as being optimal or suboptimal. Oxygenated (HbO) and deoxygenated (HbR) hemoglobin concentration changes are integrated as parts of the vector phase. We hypothesize that selecting neuronal-related HR as observed in the optimal-baseline blocks will lead to an improvement in mental workload estimation.