Complexity-based analysis of the correlation between stride interval variability and muscle reaction at different walking speeds

Decoding the correlation between heart activation and walking path by information-based analysis

BACKGROND

One of the important areas of heart research is to analyze heart rate variability during (HRV) walking.

OBJECTIVE

In this research, we investigated the correction between heart activation and the variations of walking paths.

METHOD

We employed Shannon entropy to analyze how the information content of walking paths affects the information content of HRV. Eight healthy students walked on three designed walking paths with different information contents while we recorded their ECG signals. We computed and analyzed the Shannon entropy of the R-R interval time series (as an indicator of HRV) versus the Shannon entropy of different walking paths and accordingly evaluated their relation.

RESULTS

According to the obtained results, walking on the path that contains more information leads to less information in the R-R time series.

CONCLUSION

The analysis method employed in this research can be extended to analyze the relation between other physiological signals (such as brain or muscle reactions) and the walking path.

Influence of Groundwater Depth on Pile–Soil Mechanical Properties and Fractal Characteristics under Cyclic Loading

The analysis of the behavior of soil and foundations when the piles in offshore areas are subjected to long-term lateral loading (wind) is one of the major problems associated with the smooth operation of superstructure. The strength of the pile-soil system is influenced by variations in the water content of the soil. At present, there are no studies carried out analyzing the mechanical and deformational behavior of both the material of the laterally loaded piles and soil with groundwater level as a variable. In this paper, a series of 1-g model tests were conducted to explore the lateral behavior of both soil and monopile under unidirectional cyclic loading, based on the foundation of an offshore wind turbine near the island. The influence of underground water level and cyclic load magnitude on the performance of the pile–soil system was analyzed. To visualize the movements of soil particles during the experimental process, particle image velocimetry (PIV) was used to record the soil displacement field under various cyclic loading conditions. The relationship curves between pile top displacement and cyclic steps, as well as the relationship curves between cyclic stiffness and cyclic steps, were displayed. Combined with fractal theory, the fractal dimension of each curve was calculated to evaluate the sensitivity of the pile–soil interaction system. The results showed that cyclic loading conditions and groundwater depth are the main factors affecting the pile–soil interaction. The cyclic stiffness of the soil increased in all test groups as loading progressed; however, an increase in the cyclic load magnitude decreased the initial and cyclic stiffness. The initial and cyclic stiffness of dry soil was higher than that of saturated soil, but less than that of unsaturated soil. The ability of the unsaturated soil to limit the lateral displacement of the pile decreased as the depth of the groundwater level dropped. The greater the fluctuation of the pile top displacement, the larger the fractal dimension of each relationship curve, with a variation interval of roughly 1.24–1.38. The average increment of the cumulative pile top displacement between each cycle step following the cyclic loading was positively correlated with fractal dimension. Based on the PIV results, the changes in the pile–soil system were predominantly focused in the early stages of the experiment, and the short-term effects of lateral cyclic loading are greater than the long-term effects. In addition, this research was limited to a single soil layer. The pile–soil interaction under layered soil is investigated, and the results will be used in more complex ground conditions in the future.

Decoding of the coupling between the brain and facial muscle reactions in auditory stimulation

BACKGROUND

Analysis of the reactions of different organs to external stimuli is an important area of research in physiological science.

OBJECTIVE

In this paper, we investigated the correlation between the brain and facial muscle activities by information-based analysis of electroencephalogram (EEG) signals and electromyogram (EMG) signals using Shannon entropy.

METHOD

The EEG and EMG signals of thirteen subjects were recorded during rest and auditory stimulations using relaxing, pop, and rock music. Accordingly, we calculated the Shannon entropy of these signals.

RESULTS

The results showed that rock music has a greater effect on the information of EEG and EMG signals than pop music, which itself has a greater effect than relaxing music. Furthermore, a strong correlation (r= 0.9980) was found between the variations of the information of EEG and EMG signals.

CONCLUSION

The activities of the facial muscle and brain are correlated in different conditions. This technique can be utilized to investigate the correlation between the activities of different organs versus brain activity in different situations.

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

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.

Decoding the coupling between the brain and skin reactions in auditory stimulation by information-based analysis of EEG and GSR signals

BACKGROUND

The analysis of brain activity in different conditions is an important research area in neuroscience.

OBJECTIVE

This paper analyzed the correlation between the brain and skin activities in rest and stimulations by information-based analysis of electroencephalogram (EEG) and galvanic skin resistance (GSR) signals.

METHODS

We recorded EEG and GSR signals of eleven subjects during rest and auditory stimulations using three pieces of music that were differentiated based on their complexity. Then, we calculated the Shannon entropy of these signals to quantify their information contents.

RESULTS

The results showed that music with greater complexity has a more significant effect on altering the information contents of EEG and GSR signals. We also found a strong correlation (r= 0.9682) among the variations of the information contents of EEG and GSR signals. Therefore, the activities of the skin and brain are correlated in different conditions.

CONCLUSION

This analysis technique can be utilized to evaluate the correlation among the activities of various organs versus brain activity in different conditions.

Information-based analysis of the coupling between brain and heart reactions to olfactory stimulation

BACKGROUND

Analysis of the heart activity is one of the important areas of research in biomedical science and engineering. For this purpose, scientists analyze the activity of the heart in various conditions. Since the brain controls the heart's activity, a relationship should exist among their activities.

OBJECTIVE

In this research, for the first time the coupling between heart and brain activities was analyzed by information-based analysis.

METHODS

Considering Shannon entropy as the indicator of the information of a system, we recorded electroencephalogram (EEG) and electrocardiogram (ECG) signals of 13 participants (7 M, 6 F, 18-22 years old) in different external stimulations (using pineapple, banana, vanilla, and lemon flavors as olfactory stimuli) and evaluated how the information of EEG signals and R-R time series (as heart rate variability (HRV)) are linked.

RESULTS

The results indicate that the changes in the information of the R-R time series and EEG signals are strongly correlated (ρ=-0.9566).

CONCLUSION

We conclude that heart and brain activities are related.