Using Facial Electromyography to Assess Facial Muscle Reactions to Experienced and Observed Affective Touch in Humans

Ree, Anbjørn; Morrison, India; Olausson, Håkan; Sailer, Uta; Heilig, Markus; Mayo, Leah M.

doi:10.3791/59228

Cited by 6 publications

(4 citation statements)

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“…2B ). 36 , 37 , 41 In contrast, the CIP participants showed no consistent corrugator activity to slow or fast brushing ( Fig. 2B and C ).…”

Section: Resultsmentioning

confidence: 88%

“…Touch was administered using a soft 5 cm-wide brush applied to a 9 cm section of the forearm as detailed previously. 36 , 37 The task consisted of four blocks; each block consisted of eight trials, four at each velocity, 3 cm/s (slow) and 30 cm/s (fast), with the velocity order within each block pseudo-randomized but not repeated more than three times. During the inter-trial intervals, participants were rated on a visual-analogue scale ‘How PLEASANT was the touch?’ or ‘How INTENSE was the touch?’ and could choose from −10 (extremely unpleasant) to +10 (extremely pleasant) or −10 (not at all intense) to +10 (extremely intense), respectively, using the mouse to move the visual-analogue scale slider.…”

Section: Methodsmentioning

confidence: 99%

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Nav1.7 is required for normal C-low threshold mechanoreceptor function in humans and mice

Middleton

Perini

et al. 2021

Brain

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Patients with bi-allelic loss of function mutations in the voltage-gated sodium channel Nav1.7 present with congenital insensitivity to pain (CIP), whilst low threshold mechanosensation is reportedly normal. Using psychophysics (n = 6 CIP participants and n = 86 healthy controls) and facial EMG (n = 3 CIP participants and n = 8 healthy controls) we have found that these patients also have abnormalities in the encoding of affective touch which is mediated by the specialised afferents; C-low threshold mechanoreceptors (C-LTMRs). In the mouse we found that C-LTMRs express high levels of Nav1.7. Genetic loss or selective pharmacological inhibition of Nav1.7 in C-LTMRs resulted in a significant reduction in the total sodium current density, an increased mechanical threshold and reduced sensitivity to non-noxious cooling. The behavioural consequence of loss of Nav1.7 in C-LTMRs in mice was an elevation in the von Frey mechanical threshold and less sensitivity to cooling on a thermal gradient. Nav1.7 is therefore not only essential for normal pain perception but also for normal C-LTMR function, cool sensitivity and affective touch.

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“…2B ). 36 , 37 , 41 In contrast, the CIP participants showed no consistent corrugator activity to slow or fast brushing ( Fig. 2B and C ).…”

Section: Resultsmentioning

confidence: 88%

Section: Methodsmentioning

confidence: 99%

Nav1.7 is required for normal C-low threshold mechanoreceptor function in humans and mice

Middleton

Perini

et al. 2021

Brain

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show abstract

“…This system functions on the premise that the representation of emotions through facial movement are conserved across cultures and peoples [24]. Other studies have used facial EMG to detect purposeful changes in facial expression [25], as well as involuntary movements in response to affective touch [26]. Thus, quantitative analysis of facial movements may provide an intriguing modality for studying emotion and mood states.…”

Section: Introductionmentioning

confidence: 99%

Real-time emotion detection by quantitative facial motion analysis

Saadon

Yang²,

Burgert³

et al. 2023

PLoS ONE

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Background Research into mood and emotion has often depended on slow and subjective self-report, highlighting a need for rapid, accurate, and objective assessment tools. Methods To address this gap, we developed a method using digital image speckle correlation (DISC), which tracks subtle changes in facial expressions invisible to the naked eye, to assess emotions in real-time. We presented ten participants with visual stimuli triggering neutral, happy, and sad emotions and quantified their associated facial responses via detailed DISC analysis. Results We identified key alterations in facial expression (facial maps) that reliably signal changes in mood state across all individuals based on these data. Furthermore, principal component analysis of these facial maps identified regions associated with happy and sad emotions. Compared with commercial deep learning solutions that use individual images to detect facial expressions and classify emotions, such as Amazon Rekognition, our DISC-based classifiers utilize frame-to-frame changes. Our data show that DISC-based classifiers deliver substantially better predictions, and they are inherently free of racial or gender bias. Limitations Our sample size was limited, and participants were aware their faces were recorded on video. Despite this, our results remained consistent across individuals. Conclusions We demonstrate that DISC-based facial analysis can be used to reliably identify an individual’s emotion and may provide a robust and economic modality for real-time, noninvasive clinical monitoring in the future.

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Section: Introductionmentioning

confidence: 99%

Real-time Emotion Detection by Quantitative Facial Motion Analysis

Saadon

Yang

Burgert

et al. 2022

Preprint

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Background: Research into mood and emotion has often depended on slow and subjective selfreport, highlighting a need for rapid, accurate, and objective assessment tools. Methods: To address this gap, we developed a method using digital image speckle correlation (DISC), which tracks subtle changes in facial expressions invisible to the naked eye, to assess emotions in real-time. We presented ten participants with visual stimuli triggering neutral, happy, and sad emotions and quantified their associated facial responses via detailed DISC analysis. Results: We identified key alterations in facial expression (facial maps) that reliably signal changes in mood state across all individuals based on these data. Furthermore, principal component analysis of these facial maps identified regions associated with happy and sad emotions. Compared with commercial deep learning solutions that use individual images to detect facial expressions and classify emotions, such as Amazon Rekognition, our DISC-based classifiers utilize frame-to-frame changes. Our data show that DISC-based classifiers deliver substantially better predictions, and they are inherently free of racial or gender bias. Limitations: Our sample size was limited, and participants were aware their faces were recorded on video. Despite this, our results remained consistent across individuals. Conclusions: We demonstrate that DISC-based facial analysis can be used to reliably identify an individual's emotion and may provide a robust and economic modality for real-time, noninvasive clinical monitoring in the future.

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Using Facial Electromyography to Assess Facial Muscle Reactions to Experienced and Observed Affective Touch in Humans

Cited by 6 publications

References 0 publications

Nav1.7 is required for normal C-low threshold mechanoreceptor function in humans and mice

Nav1.7 is required for normal C-low threshold mechanoreceptor function in humans and mice

Real-time emotion detection by quantitative facial motion analysis

Real-time Emotion Detection by Quantitative Facial Motion Analysis

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