Decoding of facial muscle-brain relation by information-based analysis of electromyogram (EMG) and electroencephalogram (EEG) signals

• Published in: Waves in random and complex media Vol. 34; no. 4; pp. 3599 - 3608
• Main Authors: Pakniyat, Najmeh, Soundirarajan, Mirra, Gohery, Scott, Burvill, Colin, Krejcar, Ondrej, Namazi, Hamidreza
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 03.07.2024; Taylor & Francis Ltd
• Subjects: Brain; EEG signals; Electroencephalography; Electromyography; EMG signals; Entropy (Information theory); Facial muscle; information; Information theory; Muscles; Odors; olfactory stimuli; Shannon entropy
• ISSN: 1745-5030; 1745-5049
• DOI: 10.1080/17455030.2021.1983227

To understand the connection among the brain and facial muscles, we decoded the correlation of facial muscles and brain activities. We applied information theory (Shannon entropy) to EEG and EMG signals recorded from 12 subjects (7 M, 5 F) during rest and stimulation using three odors (pineapple, banana, vanilla). The results indicated that a larger variation in the molecular complexity of odors causes a larger variation in the information of EMG and EEG signals. Therefore, facial muscles' activity is strongly coupled (r = −0.8399) to the brain's activity. Our analysis method could be further employed to examine the relationship between the brain and other organs by comparing other physiological signals with the brain signals.