Characteristics of facial muscle activity during voluntary facial expressions: Imaging analysis of facial expressions based on myogenic potential data

Kuramoto, Eriko; Yoshinaga, Saori; Nakao, Hiroyuki; Nemoto, Seiji; Ishida, Yoshiteru

doi:10.1002/npr2.12059

Cited by 18 publications

(19 citation statements)

References 20 publications

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“…Overall, both sEMG schemes allowed to record of characteristic and discriminable facial muscle activation patterns during different facial movement tasks. The geometric sEMG recording from the entire face introduced by Kuramoto et al seemed to allow more specific detection of facial muscle activity patterns during facial movement tasks ( Kuramoto et al, 2019 ). As shown by others, there was no relevant side difference ( Schumann et al, 2010 , 2021 ; Kuramoto et al, 2019 ; Cui et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

“…The sEMG recording was performed with a multichannel EMG system (gain: 100, frequency range: 10–1,861 Hz; sampling rate: 4,096/s; resolution: 5.96 nV/bit; DeMeTec, Langgöns, Germany). Electromyograms were recorded from both sides of the face simultaneously using both the arrangement of electrodes by Fridlund and Cacioppo (1986) and by Kuramoto et al (2019) (cf. Figures 1C–G ).…”

Section: Methodsmentioning

confidence: 99%

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High-resolution surface electromyographic activities of facial muscles during mimic movements in healthy adults: A prospective observational study

Mueller

Trentzsch

Grassme

et al. 2022

Front. Hum. Neurosci.

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ObjectivesSurface electromyography (sEMG) is a standard tool in clinical routine and clinical or psychosocial experiments also including speech research and orthodontics to measure the activity of selected facial muscles to objectify facial movements during specific facial exercises or experiments with emotional expressions. Such muscle-specific approaches neglect that facial muscles act more as an interconnected network than as single facial muscles for specific movements. What is missing is an optimal sEMG setting allowing a synchronous measurement of the activity of all facial muscles as a whole.MethodsA total of 36 healthy adult participants (53% women, 18–67 years) were included. Electromyograms were recorded from both sides of the face using an arrangement of electrodes oriented by the underlying topography of the facial muscles (Fridlund scheme) and simultaneously by a geometric and symmetrical arrangement on the face (Kuramoto scheme). The participants performed a standard set of different facial movement tasks. Linear mixed-effects models and adjustment for multiple comparisons were used to evaluate differences between the facial movement tasks, separately for both applied schemes. Data analysis utilized sEMG amplitudes and also their maximum-normalized values to account for amplitude differences between the different facial movements.ResultsSurface electromyography activation characteristics showed systematic regional distribution patterns of facial muscle activation for both schemes with very low interindividual variability. The statistical significance to discriminate between the different sEMG patterns was good for both schemes (significant comparisons for sEMG amplitudes: 87.3%, both schemes, normalized values: 90.9%, Fridlund scheme, 94.5% Kuramoto scheme), but the Kuramoto scheme performed considerably superior.ConclusionFacial movement tasks evoke specific patterns in the complex network of facial muscles rather than activating single muscles. A geometric and symmetrical sEMG recording from the entire face seems to allow more specific detection of facial muscle activity patterns during facial movement tasks. Such sEMG patterns should be explored in more clinical and psychological experiments in the future.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

High-resolution surface electromyographic activities of facial muscles during mimic movements in healthy adults: A prospective observational study

Mueller

Trentzsch

Grassme

et al. 2022

Front. Hum. Neurosci.

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“…A number of studies have explored the physiological basis of how pain signaling leads to pain-indicative muscle movement. Kuramoto et al recently used facial myogenic potential topography in 18 healthy adult participants to investigate the facial myogenic potential and subsequent facial expressions ( 25 ). Furthermore, Kunz used functional MRI (fMRI) to address the association between brain responses in areas that processed the sensory dimension of pain and activation of the orbicularis oculi muscle ( 26 ).…”

Section: Discussionmentioning

confidence: 99%

Deep Learning-Based Pain Classifier Based on the Facial Expression in Critically Ill Patients

Liu

et al. 2022

Front. Med.

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ObjectivePain assessment based on facial expressions is an essential issue in critically ill patients, but an automated assessment tool is still lacking. We conducted this prospective study to establish the deep learning-based pain classifier based on facial expressions.MethodsWe enrolled critically ill patients during 2020–2021 at a tertiary hospital in central Taiwan and recorded video clips with labeled pain scores based on facial expressions, such as relaxed (0), tense (1), and grimacing (2). We established both image- and video-based pain classifiers through using convolutional neural network (CNN) models, such as Resnet34, VGG16, and InceptionV1 and bidirectional long short-term memory networks (BiLSTM). The performance of classifiers in the test dataset was determined by accuracy, sensitivity, and F1-score.ResultsA total of 63 participants with 746 video clips were eligible for analysis. The accuracy of using Resnet34 in the polychromous image-based classifier for pain scores 0, 1, 2 was merely 0.5589, and the accuracy of dichotomous pain classifiers between 0 vs. 1/2 and 0 vs. 2 were 0.7668 and 0.8593, respectively. Similar accuracy of image-based pain classifier was found using VGG16 and InceptionV1. The accuracy of the video-based pain classifier to classify 0 vs. 1/2 and 0 vs. 2 was approximately 0.81 and 0.88, respectively. We further tested the performance of established classifiers without reference, mimicking clinical scenarios with a new patient, and found the performance remained high.ConclusionsThe present study demonstrates the practical application of deep learning-based automated pain assessment in critically ill patients, and more studies are warranted to validate our findings.

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“…Furthermore, the analysis was performed only in men. Although facial sEMG activation patterns were generated for men and women in some studies [6,8,17], gender differences were not yet analyzed. Therefore, we cannot rule out that women show other facial muscle expression patterns.…”

Section: Plos Onementioning

confidence: 99%

“…Fridlund and Cacioppo therefore recommended for psychophysiological experiments the sEMG recordings from ten facial muscles [7]. Recently, Kuramoto et al recommended the use of 24 sEMG electrodes in an EEG-like arrangement over the facial independent of the underlying muscles and a myogenic potential topogram analysis [8]. Experimental high-density sEMG (HD sEMG) with 90 electrodes even allows a description of facial muscle activation with activation maps projected on the facial surface [6].…”

Section: Introductionmentioning

confidence: 99%

Atlas of voluntary facial muscle activation: Visualization of surface electromyographic activities of facial muscles during mimic exercises

et al. 2021

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Complex facial muscle movements are essential for many motoric and emotional functions. Facial muscles are unique in the musculoskeletal system as they are interwoven, so that the contraction of one muscle influences the contractility characteristic of other mimic muscles. The facial muscles act more as a whole than as single facial muscle movements. The standard for clinical and psychosocial experiments to detect these complex interactions is surface electromyography (sEMG). What is missing, is an atlas showing which facial muscles are activated during specific tasks. Based on high-resolution sEMG data of 10 facial muscles of both sides of the face simultaneously recorded during 29 different facial muscle tasks, an atlas visualizing voluntary facial muscle activation was developed. For each task, the mean normalized EMG amplitudes of the examined facial muscles were visualized by colors. The colors were spread between the lowest and highest EMG activity. Gray shades represent no to very low EMG activities, light and dark brown shades represent low to medium EMG activities and red shades represent high to very high EMG activities relatively with respect to each task. The present atlas should become a helpful tool to design sEMG experiments not only for clinical trials and psychological experiments, but also for speech therapy and orofacial rehabilitation studies.

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Characteristics of facial muscle activity during voluntary facial expressions: Imaging analysis of facial expressions based on myogenic potential data

Cited by 18 publications

References 20 publications

High-resolution surface electromyographic activities of facial muscles during mimic movements in healthy adults: A prospective observational study

High-resolution surface electromyographic activities of facial muscles during mimic movements in healthy adults: A prospective observational study

Deep Learning-Based Pain Classifier Based on the Facial Expression in Critically Ill Patients

Atlas of voluntary facial muscle activation: Visualization of surface electromyographic activities of facial muscles during mimic exercises

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