Complexity-based analysis of the coupling between facial muscle and brain activities

Soundirarajan, Mirra; Aghasian, Erfan; Krejcar, Ondřej; Namazi, Hamidreza

doi:10.1016/j.bspc.2021.102511

Cited by 19 publications

(3 citation statements)

References 38 publications

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“…The activation of muscle and brain in the form of EMG and EEG signals have complex structures (Kumarasinghe et al, 2021;Soundirarajan et al, 2021). In fact, complexity is a concept to characterize the behavior of a system that contains many parts which interacting together in a highly variable way (Steven, 2001).…”

Section: Introductionmentioning

confidence: 99%

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

Pakniyat¹,

Namazi

2021

Front. Hum. Neurosci.

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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.

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Section: Introductionmentioning

confidence: 99%

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

Pakniyat¹,

Namazi

2021

Front. Hum. Neurosci.

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“…Owing to their ability to distinguish between positive and negative affect, fEMG was explored as a tool in marketing. Specifically, to objectively differentiate between positive and negative responses to different products [41,42]. While low-resolution EMG can be used to differentiate between positive and negative affect, utilizing the specificity of the zygomaticus major and the corrugator supercilii muscles respectively, high-resolution EMG may provide insight to more subtle categories [43], such as those used in questionnaires applied in market research.…”

Section: Applicationsmentioning

confidence: 99%

Wearable facial electromyography: in the face of new opportunities

2023

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Facial muscles play an important role in a vast range of physiological functions, ranging from mastication to communication. Any disruption in their normal function may lead to serious negative effects on human well-being. A very wide range of medical disorders and conditions in psychology, neurology, psychiatry, and cosmetic surgery are related to facial muscles, and scientific explorations spanning over decades exposed many fascinating phenomena. For example, expansive evidence implicates facial muscle activation with the expression of emotions. Yet, the exact manner by which emotions are expressed is still debated: Whether facial expressions are universal, how gender and cultural differences shape facial expressions and if and how facial muscle activation shape the internal emotional state. Surface electromyography (EMG) is one of the best tools for direct investigation of facial muscle activity and can be applied for medical and research purposes. The use of surface EMG has been so far restricted, owing to limited resolution and cumbersome setups. Current technologies are inconvenient, interfere with the subject normal behavior, and require know-how in proper electrode placement. High density electrode arrays, based on soft skin technology, is a recent development in the realm of surface EMG. It opens the door to perform facial EMG (fEMG) with high signal quality, while maintaining significantly more natural environmental conditions and higher data resolution. Signal analysis of multi-electrode recordings can also reduce crosstalk to achieve single muscle resolution. This perspective paper presents and discusses new opportunities in mapping facial muscle activation, brought about by this technological advancement. The paper briefly reviews some of the main applications of fEMG and presents how these applications can benefit from a more precise and less intrusive technology.

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“…On the other hand, many researchers utilized various types of entropy to analyze EEG signals. The works that evaluated the alterations of EEG signals using permutation entropy [17], approximate entropy [18], multiscale entropy [19], sample entropy [20], fuzzy entropy [21], and Shannon entropy [22] are worthy of being mentioned.…”

Section: Introductionmentioning

confidence: 99%

Decoding of the coupling between brain and skin activities in olfactory stimulation by analysis of EEG and GSR signals

Omam

Babini

Sim

et al. 2021

Waves in Random and Complex Media

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Our skin reacts to various stimuli that we receive. Since all parts of the human body are controlled by the brain, a relationship should exist among brain and skin activities. This study evaluates the relation among skin and brain activities. As such, we benefited from the information-based analysis. We collected GSR and EEG signals of eight participants in various olfactory stimulations. Accordingly, we ran Shannon entropy-based analysis to evaluate the correlation between the information contents of these two signals. The results showed that the alterations in the complexity of stimulus and the information of GSR and EEG signals are strongly correlated. We also verified the results of the analysis of Shannon entropy of signals by calculating their Hurst exponent to quantify their memory. According to the results, the alterations of the memory of EEG and GSR signals are similar to the alterations of the information of these signals.

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Complexity-based analysis of the coupling between facial muscle and brain activities

Cited by 19 publications

References 38 publications

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

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

Wearable facial electromyography: in the face of new opportunities

Decoding of the coupling between brain and skin activities in olfactory stimulation by analysis of EEG and GSR signals

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