Predicting 3D lip shapes using facial surface EMG

Eskes, Merijn; Alphen, M.J.A. van; Balm, Alfons J. M.; Smeele, Ludi E.; Brandsma, Dieta; Heijden, Ferdinand van der

doi:10.1371/journal.pone.0175025

Cited by 13 publications

(15 citation statements)

References 23 publications

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“…We notice that Eskes et al (2017) have indeed observed the dissociation we initially predicted, that is, that the EMG amplitude recorded over the OOI was higher during the pronunciation of rounded vowels than during pronunciation of spread vowels, whereas the reverse pattern was observed concerning the ZYG. Paired-samples Wilcoxon signed rank tests revealed a shift in location (pseudomedian) between rounded and spread items for the OOI (β = 24.12,95% CI [15.19,40.77], V = 1184, p < .001) with rounded items being associated with a higher location than spread items.…”

Section: Discussionsupporting

confidence: 73%

“…This analysis also revealed a shift in the inverse direction concerning the ZYG (β = -1.51, 95% CI [-2.94, -0.48], V = 275, p < .001). However, one crucial difference between Eskes et al (2017) design and ours is the complexity of the linguistic material. Whereas Eskes et al (2017) used single phonemes, we chose to use bisyllabic nonwords to increase the ecological validity of the paradigm.…”

Section: Discussionmentioning

confidence: 94%

“…However, one crucial difference between Eskes et al (2017) design and ours is the complexity of the linguistic material. Whereas Eskes et al (2017) used single phonemes, we chose to use bisyllabic nonwords to increase the ecological validity of the paradigm. Although these nonwords were specifically created to theoretically increase the engagement of either the OOI or the ZYG (cf.…”

Section: Discussionmentioning

confidence: 94%

“…To answer the first question, we began by comparing our results to results obtained by another group (Eskes et al, 2017). The authors of this study recorded surface EMG activity from five participants while they were producing seven facial expressions and five isolated vowel sounds (/a/, /e/, /i/, /o/, /u/), repeated five times each.…”

Section: Discussionmentioning

confidence: 99%

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Can we decode phonetic features in inner speech using surface electromyography?

Nalborczyk¹,

Grandchamp²,

Koster³

et al. 2019

Preprint

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Although having a long history of scrutiny in experimental psychology, it is still controversial whether inner speech (covert speech) production is accompanied by specific activity in speech muscles. We address this question by briefly reviewing previous findings related to inner speech and to the broader phenomenon of motor imagery. We then present the results of a preregistered experiment looking at the electromyographic correlates of both overt speech and inner speech production of two phonetic classes of nonwords. An automatic classification approach was undertaken to discriminate between two articulatory features contained in nonwords uttered in both overt and covert speech. Although this approach led to reasonable accuracy rates during overt speech production, it failed to discriminate inner speech phonetic content based on surface electromyography signals alone. However, exploratory analyses conducted at the individual level revealed that it seemed possible to distinguish between rounded and spread nonwords covertly produced, in two participants. We discuss these results in relation to the existing literature and suggest alternative ways to test the engagement of the speech motor system during inner speech production. Pre-registered protocol, preprint, data, as well as reproducible code and figures are available at: https://osf.io/czer4/.

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Section: Discussionsupporting

confidence: 73%

Section: Discussionmentioning

confidence: 94%

Section: Discussionmentioning

confidence: 94%

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Can we decode phonetic features in inner speech using surface electromyography?

Nalborczyk¹,

Grandchamp²,

Koster³

et al. 2019

Preprint

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show abstract

“…In previous research, we found the following procedure to be optimal for transforming measured sEMG signals into normalised muscle activations 5 – 7 . We first calculated the Willison Amplitude with over sliding windows of 200 ms with maximum overlap: …”

Section: Data Acquisition and Pre-processingmentioning

confidence: 99%

sEMG-assisted inverse modelling of 3D lip movement: a feasibility study towards person-specific modelling

Eskes

Balm

Alphen

et al. 2017

Sci Rep

Self Cite

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We propose a surface-electromyographic (sEMG) assisted inverse-modelling (IM) approach for a biomechanical model of the face to obtain realistic person-specific muscle activations (MA) by tracking movements as well as innervation trajectories. We obtained sEMG data of facial muscles and 3D positions of lip markers in six volunteers and, using a generic finite element (FE) face model in ArtiSynth, performed inverse static optimisation with and without sEMG tracking on both simulation data and experimental data. IM with simulated data and experimental data without sEMG data showed good correlations of tracked positions (0.93 and 0.67) and poor correlations of MA (0.27 and 0.20). When utilising the sEMG-assisted IM approach, MA correlations increased drastically (0.83 and 0.59) without sacrificing performance in position correlations (0.92 and 0.70). RMS errors show similar trends with an error of 0.15 in MA and of 1.10 mm in position. Therefore, we conclude that we were able to demonstrate the feasibility of an sEMG-assisted inverse modelling algorithm for the perioral region. This approach may help to solve the ambiguity problem in inverse modelling and may be useful, for instance, in future applications for preoperatively predicting treatment-related function loss.

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Correlation of 3D Morphometric Changes, Kinematics, and Muscle Activity During Smile

Özsoy,

Yıldırım

et al. 2024

The Laryngoscope

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ObjectiveKnowing the morphological, kinematic, and electrophysiological parameters of the smile in healthy individuals may contribute to evaluating, planning, and monitoring the smile reanimation. This study aimed to determine the correlation between 3D morphometric changes, movement kinematics, and muscle activity in the facial soft tissue of healthy individuals.MethodIn this cohort study, 20 volunteers were selected from healthy individuals with no facial disorders. During smiling, three‐dimensional face scanning, facial motion capture, and surface electromyography (sEMG) were performed. The average displacement, velocity, and acceleration during facial movements were measured. The mean change in 3D surface morphometry and activation of the zygomaticus major were determined.ResultsThe volunteers, comprising 10 males and 10 females, had a mean age of 24 ± 10 years; for female, mean age was 23 ± 5 years and for men 26 ± 13 years. Significant correlations were found between kinematic and morphometric data (r = 0.51, p < 0.001), sEMG and morphometric (r = 0.50, p < 0.001) data, and sEMG and kinematic data (r = 0.49, p < 0.002). The maximum acceleration occurred during approximately 65% of the muscle activation time and 64% of the peak muscle activation value. Additionally, the maximum velocity was reached at around 73% of the muscle activation time and 67% of the peak muscle activation value. Furthermore, the maximum displacement values were observed at approximately 88% of the muscle activation time and 76% of the peak muscle activation value.ConclusionThe findings may provide insights into the smile's functional parameters, contribute to understanding facial muscle‐related disorders, and aid in improving the diagnosis and treatment of the smile.Level of EvidenceN/A Laryngoscope, 2024

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Predicting 3D lip shapes using facial surface EMG

Cited by 13 publications

References 23 publications

Can we decode phonetic features in inner speech using surface electromyography?

Can we decode phonetic features in inner speech using surface electromyography?

sEMG-assisted inverse modelling of 3D lip movement: a feasibility study towards person-specific modelling

Correlation of 3D Morphometric Changes, Kinematics, and Muscle Activity During Smile

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