Complexity-Based Analysis of the Variations of Brain and Muscle Reactions in Walking and Standing Balance While Receiving Different Perturbations

PAKNIYAD, Najmeh and Hamidreza NAMAZI. Complexity-Based Analysis of the Variations of Brain and Muscle Reactions in Walking and Standing Balance While Receiving Different Perturbations. Frontiers in Human Neuroscience. Lausanne (Schwitzerland): FRONTIERS MEDIA SA, 2021, vol. 15, No 749082, p. nestránkováno, 10 pp. ISSN 1662-5161. Available from: https://dx.doi.org/10.3389/fnhum.2021.749082.

Basic information

• Original name: Complexity-Based Analysis of the Variations of Brain and Muscle Reactions in Walking and Standing Balance While Receiving Different Perturbations
• Authors: PAKNIYAD, Najmeh (364 Islamic Republic of Iran) and Hamidreza NAMAZI (364 Islamic Republic of Iran, guarantor, belonging to the institution).
• Edition: Frontiers in Human Neuroscience, Lausanne (Schwitzerland), FRONTIERS MEDIA SA, 2021, 1662-5161.

Other information

• Original language: English
• Type of outcome: Article in a journal
• Field of Study: 30306 Sport and fitness sciences
• Country of publisher: Switzerland
• Confidentiality degree: is not subject to a state or trade secret
• WWW: URL
• Impact factor: Impact factor: 3.473
• RIV identification code: RIV/00216224:14510/21:00122673
• Organization unit: Faculty of Sports Studies
• Doi: http://dx.doi.org/10.3389/fnhum.2021.749082
• UT WoS: 000710920900001
• Keywords in English: muscle; brain; EEG signals; EMG signals; complexity; walking; standing; perturbations
• Tags: rivok
• Tags: International impact, Reviewed

Abstract

In this article, we evaluated the variations of the brain and muscle activations while subjects are exposed to different perturbations to walking and standing balance. Since EEG and EMG signals have complex structures, we utilized the complexity-based analysis. Specifically, we analyzed the fractal dimension and sample entropy of Electroencephalogram (EEG) and Electromyogram (EMG) signals while subjects walked and stood, and received different perturbations in the form of pulling and rotation (via virtual reality). The results showed that the complexity of EEG signals was higher in walking than standing as the result of different perturbations. However, the complexity of EMG signals was higher in standing than walking as the result of different perturbations. Therefore, the alterations in the complexity of EEG and EMG signals are inversely correlated. This analysis could be extended to investigate simultaneous variations of rhythmic patterns of other physiological signals while subjects perform different activities.