A hybrid feature pool-based emotional stress state detection algorithm using EEG signals.

Hasan, Md. Junayed; Kim, Jong-Myon

Abstract

Human stress analysis using electroencephalogram (EEG) signals requires a detailed and domain‐specific information pool to develop an effective machine learning model. In this study, a multi‐domain hybrid feature pool is designed to identify most of the important information from the signal. The hybrid feature pool contains features from two types of analysis: (a) statistical parametric analysis from the time domain, and (b) wavelet‐based bandwidth specific feature analysis from the time‐frequency domain. Then, a wrapper‐based feature selector, Boruta, is applied for ranking all the relevant features from that feature pool instead of considering only the nonredundant features. Finally, the k‐nearest neighbor (k‐NN) algorithm is used for final classification. The proposed model yields an overall accuracy of 73.38% for the total considered dataset. To validate the performance of the proposed model and highlight the necessity of designing a hybrid feature pool, the model was compared to non‐linear dimensionality reduction techniques, as well as those without feature ranking.

Citation

HASAN, M.J. and KIM, J.-M. 2019. A hybrid feature pool-based emotional stress state detection algorithm using EEG signals. Brain sciences [online], 9(12), article number 376. Available from: https://doi.org/10.3390/brainsci9120376

• Journal Article Type: Article
• Acceptance Date: Dec 12, 2019
• Online Publication Date: Dec 13, 2019
• Publication Date: Dec 31, 2019
• Deposit Date: May 13, 2022
• Publicly Available Date: May 16, 2022
• Journal: Brain sciences
• Electronic ISSN: 2076-3425
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 9
• Issue: 12
• Article Number: 376
• DOI: https://doi.org/10.3390/brainsci9120376
• Keywords: Electroencephalogram (EEG) signals; Stress (Psychology); k-nearest neighbour (k-NN) alogrithms; Machine learning; Neurological analysis
• Public URL: https://rgu-repository.worktribe.com/output/1664503