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

Pakniyat, Najmeh; Namazi, Hamidreza

doi:10.3389/fnhum.2021.749082

Cited by 12 publications

(1 citation statement)

References 41 publications

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“…For this reason, SampEn has been calculated with a smaller number of data points or with using a continuous signal [ 13 ]. Various forms of continuous data [ 2 , 8 , 10 ] have been used in complexity analysis, however, the usage of center of pressure (COP) signal during a steady-state, where many consecutive steps can be captured, is becoming more popular [ 9 , 14 – 16 ]. The COP at a given moment in time is the location of resultant vertical ground reaction force vector.…”

Section: Introductionmentioning

confidence: 99%

The influence of walking speed and effects of signal processing methods on the level of human gait regularity during treadmill walking

Brachman

Sobota

Bacik

2022

BMC Sports Sci Med Rehabil

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Background In recent years the use of sample entropy (SampEn) to evaluate the complexity of the locomotor system in human gait data has gained in popularity. However, it has been suggested that SampEn is sensitive to various input parameters and signal preprocessing methods. This study quantified the effects of different temporal and spatial normalization approaches and various lengths of the template vector (m) on SampEn calculations. The discriminatory ability of SampEn was studied by comparing two walking conditions. Methods Twenty-three participants (seven males, 55.7 ± 8.5 years, 165.7 ± 7.9 cm, 80.5 ± 16.7 kg) walked on a treadmill with preferred (Vpref) and maximum (Vmax) speed. Data were segmented and resampled (SEGM), resampled and spatially normalized (NORM), resampled and detrended (ZERO). Results For vertical ground reaction force (vGRF) and center of pressure in anterio-posterior direction (COPap), in both walking conditions, SampEn was generally sensitive to the vector length and not to the data processing, except for COPap in ZERO, m = 2, 4. For the COPml SampEn behaved oppositely, it was sensitive to preprocessing method and not to the m length. The regularity of COPap and vGRF in all processed signals increased in Vmax condition. For the COPml only two signals, WHOLE and ZERO, revealed increased complexity caused by more demanding walking conditions. Conclusions SampEn was able to discriminate between different walking conditions in all analyzed variables, but not in all signals. Depending on evaluated variable, SampEn was susceptible in different way for the m level and processing method. Hence, these should be checked and selected for each variable independently. For future studies evaluating influence of walking velocity on COP and vGRF regularity during treadmill walking it is advised to use raw time series. Furthermore, to maintain template vector which represents biological relevance it is advised to detect highest frequencies present in analyzed signals and evaluate minimal time interval which can reflect change caused by response of a neuromuscular system. During evaluating treadmill walking measured with 100 Hz sampling frequency it is recommended to adopt m from 6 to 10, when average stride time is up to about 1 s.

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Section: Introductionmentioning

confidence: 99%

The influence of walking speed and effects of signal processing methods on the level of human gait regularity during treadmill walking

Brachman

Sobota

Bacik

2022

BMC Sports Sci Med Rehabil

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Complexity and Information-Based Analysis of the Electroencephalogram (Eeg) Signals in Standing, Walking, and Walking With a Brain–computer Interface

Ramadoss¹,

Dawi²,

Rajagopal³

et al. 2021

Fractals

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In this paper, we analyzed the variations in brain activation between different activities. Since Electroencephalogram (EEG) signals as an indicator of brain activation contain information and have complex structures, we employed complexity and information-based analysis. Specifically, we used fractal theory and Shannon entropy for our analysis. Eight subjects performed three different activities (standing, walking, and walking with a brain–computer interface) while their EEG signals were recorded. Based on the results, the complexity and information content of EEG signals have the greatest and smallest values in walking and standing, respectively. Complexity and information-based analysis can be applied to analyze the activations of other organs in different conditions.

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Decoding the Correlation Between Brain and Heart Activations in the Combination of Mental Workload and Physical Activity

Ramadoss

Pakniyat²,

Rajagopal

et al. 2022

Fractals

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Decoding of the coupling among the brain and heart activations is an important research area in network physiology. We studied the coupling of brain and heart activations for 48 subjects who performed the NASA Revised Multi-Attribute Task Battery II under three different activity level conditions. During the experiment, the physical activity of subjects was manipulated by changing the speed of a stationary bike (including no movement, 50[Formula: see text]rpm, and 70[Formula: see text]rpm) or a treadmill (including no movement, 3[Formula: see text]km/h, and 5[Formula: see text]km/h), while their physiological signals were recorded. We analyzed the complex structure of electroencephalogram (EEG) and R-R signals using fractal theory and sample entropy. The results demonstrated that the alterations of the complex structures of EEG and R-R signals are strongly correlated, which indicates the coupling between brain and heart activations. This method of analysis can be applied to evaluate the coupling between different organs.

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

Cited by 12 publications

References 41 publications

The influence of walking speed and effects of signal processing methods on the level of human gait regularity during treadmill walking

The influence of walking speed and effects of signal processing methods on the level of human gait regularity during treadmill walking

Complexity and Information-Based Analysis of the Electroencephalogram (Eeg) Signals in Standing, Walking, and Walking With a Brain–computer Interface

Decoding the Correlation Between Brain and Heart Activations in the Combination of Mental Workload and Physical Activity

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