Babiker, A. and Faye, I. (2021) A Hybrid EMD-Wavelet EEG Feature Extraction Method for the Classification of Students' Interest in the Mathematics Classroom. Computational Intelligence and Neuroscience, 2021. ISSN 16875265
Full text not available from this repository.Abstract
Situational interest (SI) is one of the promising states that can improve student's learning and increase the acquired knowledge. Electroencephalogram-(EEG-) based detection of SI could assist in understanding SI neuroscientific causes that, as a result, could explain the SI role in student's learning. In this study, 26 participants were selected based on questionnaires to participate in the mathematics classroom experiment. SI and personal interest (PI) questionnaires along with knowledge tests were undertaken to measure student's interest and knowledge levels. A hybrid method combining empirical mode decomposition (EMD) and wavelet transform was developed and employed for feature extraction. The proposed method showed significant difference using the multivariate analysis of variance (MANOVA) test and consistently outperformed other methods in the classification performance using weighted k-nearest neighbours (wkNN). The high classification accuracy of 85.7 with the sensitivity of 81.8 and specificity of 90 revealed that brain oscillation patterns of high SI students are somewhat different than students with low or no SI. In addition, the result suggests that the delta rhythm could have a significant effect on cognitive processing. © 2021 Areej Babiker and Ibrahima Faye.
Item Type: | Article |
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Additional Information: | cited By 5 |
Uncontrolled Keywords: | Biomedical signal processing; Electroencephalography; Extraction; Feature extraction; Multivariant analysis; Nearest neighbor search; Neurophysiology; Surveys; Wavelet decomposition, Classification accuracy; Classification performance; Cognitive processing; Electro-encephalogram (EEG); Empirical Mode Decomposition; Feature extraction methods; K-nearest neighbours; Multivariate analysis of variances, Students, brain; electroencephalography; human; mathematics; student; wavelet analysis, Brain; Electroencephalography; Humans; Mathematics; Students; Wavelet Analysis |
Depositing User: | Mr Ahmad Suhairi UTP |
Date Deposited: | 10 Nov 2023 03:30 |
Last Modified: | 10 Nov 2023 03:30 |
URI: | https://khub.utp.edu.my/scholars/id/eprint/15867 |