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Prediction of ADHD from a small dataset using an adaptive EEG Theta/Beta Ratio and PCA feature extraction

Sase, Takumi and Othman, Marini (2022) Prediction of ADHD from a small dataset using an adaptive EEG Theta/Beta Ratio and PCA feature extraction. In: Proceedings of the Fifth International Conference on Soft Computing and Data Mining (SCDM), 30-31 May 2022, UTHM, (Virtual).

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EEG Theta/beta ratio (TBR) is conventionally used as a biomarker in childhood Attention-Deficit/Hyperactivity Disorder (ADHD) prediction and treatment. Due to the heterogeneity of ADHD symptoms, several studies have applied machine learning algorithms for enhancing the recognition of ADHD. These methods, however, have limited performance in a small dataset. In this paper, we propose an adaptive EEG feature extraction approach using TBR and PCA. Repeated TBR-PCA feature extraction, SVM classification and statistical testing were applied on a small EEG sample with ADHD/typically developing (TD) labels. The steps were repeated with an update of the feature extraction technique until a high accuracy is achieved, allowing the small samples to be correctly identified (r = 0.833, one-sided, Bonferroni-corrected p < 0.0166). Within subjects EEG samples analyses performed better compared to between subject analyses, with accuracy getting worse with the increase of EEG segments. The contribution of this work is two-fold: the practical application allows for a reliable adoption of machine learning in non-invasive EEG screening of small ADHD dataset, while the theoretical contribution extends beyond the eyes closed resting state condition considered in this study and provides a methodological approach when working with limited samples

Item Type: Conference or Workshop Item (Plenary Papers)
Subjects: R Medicine > RC Internal medicine > RC321 Neuroscience. Biological psychiatry. Neuropsychiatry
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Information and Communication Technology
Kulliyyah of Information and Communication Technology
Depositing User: MARINI OTHMAN
Date Deposited: 04 Jul 2022 09:09
Last Modified: 04 Jul 2022 09:15
URI: http://irep.iium.edu.my/id/eprint/98541

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