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.
Highlights
Analysis of the alterations in human physiology during different locomotion is very important in sport sciences
The results showed that the complexity of EEG signals was higher in walking than standing as the result of different perturbations
We investigated the variations of brain and leg muscle reactions in walking and standing while subjects received different perturbations in the form of pulling and visual rotation
Summary
Analysis of the alterations in human physiology during different locomotion is very important in sport sciences. The fractal theory has been widely applied in the analysis of the complexity of EEG signals in different conditions [e.g., external stimulation (Babini et al, 2020), detection of brain disorders (Namazi et al, 2020)], based on our search, only one reported work analyzed EEG signals during locomotion using fractal theory. Many reported works analyzed the complex structure of EEG signals in different conditions [e.g., response to different stimuli (Namazi et al, 2021a), classifying brain disorders (Simons et al, 2015)] using sample entropy, no reported work evaluated the complexity of EEG signals during walking or running using sample entropy. Since no work has analyzed the complexity of EEG and EMG signals simultaneously during walking/running, we utilized fractal theory and sample entropy to evaluate the synchronization of the changes in EEG and EMG signals at different standing and walking conditions
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