Harnessing EEG Data to Explore Stress Reduction through Hypnosis
International Journal of Electronics and Communication Engineering |
© 2023 by SSRG - IJECE Journal |
Volume 10 Issue 12 |
Year of Publication : 2023 |
Authors : Swati Kamthekar, Brijesh Iyer, Prachi Deshpande, Manjiri Gokhale |
How to Cite?
Swati Kamthekar, Brijesh Iyer, Prachi Deshpande, Manjiri Gokhale, "Harnessing EEG Data to Explore Stress Reduction through Hypnosis," SSRG International Journal of Electronics and Communication Engineering, vol. 10, no. 12, pp. 83-92, 2023. Crossref, https://doi.org/10.14445/23488549/IJECE-V10I12P108
Abstract:
The rising prevalence of stress in today’s fast-paced world has incited individuals to seek complementary and alternative methods, such as yoga, meditation, and hypnosis, to find solace and peacefulness. Allopathic medicines are increasingly being shunned due to their adverse side effects. Hypnotic susceptibility assessment and stress reduction analysis are critical areas of research. To date, the hypnotic susceptibility was measured with a scale. That scale score was used for the supervised classification of the hypnotic susceptibility of the subject using EEG-like inputs. Scale scores are subjective and, hence, may be prone to errors. This paper reports a novel approach that utilizes only EEG-based evaluation of hypnotic susceptibility. The subjects classified with medium and high susceptibility were considered for stress reduction and EEG analysis using hypnosis. For hypnotic susceptibility analysis, Redefined Composite Multiscale Dispersion Entropy (RCMDE) and Multivariate Dispersion Entropy (MvDE), along with an unsupervised K-means classifier, were used. For stress analysis, alpha and beta asymmetry are used as features. The results obtained from this approach deliver valuable insights into hypnotic susceptibility and stress reduction, contributing to the advancement of EEG-based assessment methods.
Keywords:
Alpha beta asymmetry, Complementary medicine, Entropy, EEG signal, Multivariate, Stress reduction.
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