Citation

Toa, Chean Khim (2023) Assessment of brain attentive level using visual-based mixed reality and electroencephalogram. PhD thesis, Multimedia University.

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Abstract

This study aims to assess and analyse a person’s attention and distraction responses during performing a task. There are several ways of assessing those cognitive processes and Electroencephalogram (EEG) was chosen for its ability to provide direct and valuable information about the brain activity. In the past, numerous related works employed Desktop-based cognitive tests to induce attentionrelated cognitive functions in individuals and employed machine learning approaches to classify these functions based on the EEG data. However, there are two research gaps that require further exploration. The first gap involves the environment used for cognitive tests, in which the Desktop-based approach limits the participant’s immersion and engagement toward performing the test. The second gap involves the machine learning approach, where the use of feature engineering was time consuming due to the need of selecting the most suitable feature extraction method. Moreover, there is also the risk of human error when performing the feature selection, resulting in sub-optimal features selected and negatively impacting the classification performance. Therefore, to address these research gaps, the objective of this study was to first design an experimental testing framework that applied a Visual Search test to induce attention and distraction responses, while simultaneously recording their corresponding EEG signals. The study then further involved with the development of a Deep Learning (DL) model capable of learning from EEG data, selecting significant features and classifying them into their respective responses using an end-to-end approach. In the testing framework, previous studies typically perform the test in the Desktop-based environment to collect the EEG data. This study proposed the use of Mixed Reality (MR)-based environment, where the virtual objects of cognitive tests were rendered in a real environment to induce the participant’s respective responses. A comparison of the engagement, concentration, and immersion of participants performing the cognitive tests in both Desktop-based and MR-based environments was conducted through the analysis of collected EEG signals.

Item Type:	Thesis (PhD)
Additional Information:	Call No.: QA76.9.H85 T63 2023
Uncontrolled Keywords:	Mixed reality
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	29 Aug 2024 06:04
Last Modified:	29 Aug 2024 06:04
URII:	http://shdl.mmu.edu.my/id/eprint/12887

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