OPTIMIZING MENTAL WORKLOAD ESTIMATION BY DETECTING BASELINE STATE USING VECTOR PHASE ANALYSIS APPROACH
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...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | http://utpedia.utp.edu.my/id/eprint/24722/1/Lim%20Lam%20Ghai_13854.pdf http://utpedia.utp.edu.my/id/eprint/24722/ |
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| Institution: | Universiti Teknologi Petronas |
| Language: | English |
| Summary: | 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. |
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