Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/137111
Title: EEG-based mental workload analysis for multitasking testing and training systems
Authors: Lim, Wei Lun
Keywords: Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
Engineering::Computer science and engineering::Computing methodologies::Pattern recognition
Issue Date: 2020
Publisher: Nanyang Technological University
Source: Lim, W. L. (2020). EEG-based mental workload analysis for multitasking testing and training systems. Doctoral thesis, Nanyang Technological University, Singapore.
Abstract: The mental workload and multitasking capacity of an individual is an important consideration for operator and workplace safety assessment. Developing ways to understand and accurately assess mental workload is therefore essential. In this thesis, we study multitasking mental workload elicited from the simultaneous capacity (SIMKAP) psychology test, measured with the electroencephalograph (EEG) signal. Beginning with the individual specific case, we propose novel feature based methods for classification, drawing inspiration from previous work and psychophysiology. We then study general underlying neural mechanics of multitasking through EEG spectral analysis of the SIMKAP test and propose a subject independent classification model based on questionnaire ratings. Next, we consider generalization capability by transferring models trained on SIMKAP to classify a separate workload dataset and show that a novel 2-level autoencoder structure is able to learn features for stable transfer classification performance. Finally, we show applications developed for multitasking assessment and neurofeedback training.
URI: https://hdl.handle.net/10356/137111
DOI: 10.32657/10356/137111
Rights: This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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