Hurst Exponent Based Brain Behavior Analysis of Stroke Patients Using EEG Signals

Choong, Wen Yean; Khairunizam, Wan; Murugappan, M.; Omar, Mohammad Iqbal; Bong, Siao Zheng; Junoh, Ahmad Kadri; Razlan, Z. M.; Shahriman, A. B.; Mustafa, Wan Azani

doi:10.1007/978-981-15-5281-6_66

Cited by 3 publications

(1 citation statement)

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“…E MOTION recognition plays a vital role in affective computing, which identifies the human emotional states from behavioral activities or physiological signals. Accurately recognizing the human's emotional states significantly improve the reliability and intelligence level in human-computer interaction [1], [2], healthcare monitoring [3], [4], and behavior Y. Yan, X. Wu, C. Li, H. Li, and L. Wang are with the Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China. E-mail: {yan.yan, xk.wu, cd.li, hh.li, wang.lei}@siat.ac.cn Y.…”

Section: Introductionmentioning

confidence: 99%

Topological EEG Nonlinear Dynamics Analysis for Emotion Recognition

Yan,

Wu,

et al. 2022

Preprint

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Emotional recognition through exploring the electroencephalography (EEG) characteristics has been widely performed in recent studies. Nonlinear analysis and feature extraction methods for understanding the complex dynamical phenomena are associated with the EEG patterns of different emotions. The phase space reconstruction is a typical nonlinear technique to reveal the dynamics of the brain neural system. Recently, the topological data analysis (TDA) scheme has been used to explore the properties of space, which provides a powerful tool to think over the phase space. In this work, we proposed a topological EEG nonlinear dynamics analysis approach using the phase space reconstruction (PSR) technique to convert EEG time series into phase space, and the persistent homology tool explores the topological properties of the phase space. We perform the topological analysis of EEG signals in different rhythm bands to build emotion feature vectors, which shows high distinguishing ability. We evaluate the approach with two well-known benchmark datasets, the DEAP and DREAMER datasets. The recognition results achieved accuracies of 99.37% and 99.35% in arousal and valence classification tasks with DEAP, and 99.96%, 99.93%, and 99.95% in arousal, valence, and dominance classifications tasks with DREAMER, respectively. The performances are supposed to be outperformed current state-of-art approaches in DREAMER (improved by 1% to 10% depends on temporal length), while comparable to other related works evaluated in DEAP. The proposed work is the first investigation in the emotion recognition oriented EEG topological feature analysis, which brought a novel insight into the brain neural system nonlinear dynamics analysis and feature extraction.

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