No abstract

Since the brain regulates our facial reactions, there should be a relationship between their activities. Moving (dynamic) visual stimuli are an important type of visual stimuli that we are dealing with in our daily life. Since EMG and EEG signals contain information, we evaluated the coupling of the reactions of facial muscles and brain to various moving visual stimuli by analysis of the embedded information in these signals. We benefited from Shannon entropy to quantify the information. The results showed that a decrement in the information of visual stimulus is mapped on a decrement of the information of EMG and EEG signals, and therefore, the activities of facial muscles and the brain are correlated (Pearson correlation [Formula: see text]). Besides, the analysis of the Hurst exponent of EEG signals demonstrated that increasing the information of EEG signals causes the increment in its memory. This method can also be used to evaluate the coupling among other organs’ activity and brain activity by analysis of related physiological signals.

In this research, for the first time, we analyze the relationship between facial muscles and brain activities when human receives different dynamic visual stimuli. We present different moving visual stimuli to the subjects and accordingly analyze the complex structure of electromyography (EMG) signal versus the complex structure of electroencephalography (EEG) signal using fractal theory. Based on the obtained results from analysis, presenting the stimulus with greater complexity causes greater change in the complexity of EMG and EEG signals. Statistical analysis also supported the results of analysis and showed that visual stimulus with greater complexity has greater effect on the complexity of EEG and EMG signals. Therefore, we showed the relationship between facial muscles and brain activities in this paper. The method of analysis in this research can be further employed to investigate the relationship between other human organs’ activities and brain activity.

BACKGROUND: Human facial muscles react differently to different visual stimuli. It is known that the human brain controls and regulates the activity of the muscles. OBJECTIVE: In this research, for the first time, we investigate how facial muscle reaction is related to the reaction of the human brain. METHODS: Since both electromyography (EMG) and electroencephalography (EEG) signals, as the features of muscle and brain activities, contain information, we benefited from the information theory and computed the Shannon entropy of EMG and EEG signals when subjects were exposed to different static visual stimuli with different Shannon entropies (information content). RESULTS: Based on the obtained results, the variations of the information content of the EMG signal are related to the variations of the information content of the EEG signal and the visual stimuli. Statistical analysis also supported the results indicating that the visual stimuli with greater information content have a greater effect on the variation of the information content of both EEG and EMG signals. CONCLUSION: This investigation can be further continued to analyze the relationship between facial muscle and brain reactions in case of other types of stimuli.

Analysis of the coupling among the brain and various organs activities is an exciting and new research area of biomedical signal analysis. We decode the correlation among the brain and facial muscle activities during rest and visual stimulation. Fractal analysis is utilized to evaluate the alterations of complexity. In this study, we present different images with different complexities to participants and evaluate how complex structures of electromyography (EMG) signals and electroencephalography (EEG) signals are related. The findings indicate that the alteration of EMG signals' complexity was significant (P value = .0001). Besides, more significant alterations in the EMG and EEG signals' complexities in response to the stimuli with higher complexities were observed. We conclude that the brain and facial muscles activities are related. We can analyze other physiological signals using this method to investigate their relationship with the brain.