Citation

Lim, Zheng You and Sim, Kok Swee and Tan, Shing Chiang (2021) Metric Learning Based Convolutional Neural Network for Left-Right Brain Dominance Classification. IEEE Access, 9. pp. 120551-120566. ISSN 2169-3536

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Abstract

The educational concepts upholding the theory of brain dominance have been developed for more than 30 years. Some academicians developed a series of the syllabus to exploit the brain capability of students by training their weaker hemisphere of the brain. Prior to training the weaker side of the brain with the developed syllabus, the brain dominance of the student shall be determined. All the current methods used to determine brain dominance are questionnaire-based assessments. There is a possibility that questionnaire biases could exist and lead to inaccurate results. In this research, we introduce a deep-learning method to classify brain dominance based on the electroencephalogram (EEG) signal that reflects the bio-information of the brain. In this paper, we employ a series of EEG signal processing techniques and a state-of-the-art deep learning neural network namely Metric Learning Based Convolutional Neural Network (MLBCNN) to determine brain dominance. We prove that the brain dominance theory is valid and it can be determined by applying machine learning from the EEG signals. We also present the results that show the MLBCNN system can give the best performance as compared to the other benchmark neural network models of which its classification accuracy is 97.44%. Hence, this proposed method can contribute to the education field by providing a system to discover students’ brain dominance and keep track of their brain training progress. In this way, the potential and capability of their brain can be fully unleashed.

Item Type:	Article
Uncontrolled Keywords:	Electroencephalography, Training, Measurement, Convolutional neural networks
Subjects:	Q Science > QP Physiology
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	03 Oct 2021 14:14
Last Modified:	03 Oct 2021 14:14
URII:	http://shdl.mmu.edu.my/id/eprint/9601

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