Facial Electromyogram Research Articles

Music therapy with adult burn patients in the intensive care unit: short-term analysis of electrophysiological signals during music-assisted relaxation

Burn patients often face elevated pain, anxiety, and depression levels. Music therapy adds to integrative care in burn patients, but research including electrophysiological measures is limited. This study reports electrophysiological signals analysis during Music-Assisted Relaxation (MAR) with burn patients in the Intensive Care Unit (ICU). This study is a sub-analysis of an ongoing trial of music therapy with burn patients in the ICU. Electroencephalogram (EEG), electrocardiogram (ECG), and electromyogram (EMG) were recorded during MAR with nine burn patients. Additionally, background pain levels (VAS) and anxiety and depression levels (HADS) were assessed. EEG oscillation power showed statistically significant changes in the delta (p < 0.05), theta (p = 0.01), beta (p < 0.05), and alpha (p = 0.05) bands during music therapy. Heart rate variability tachograms high-frequencies increased (p = 0.014), and low-frequencies decreased (p = 0.046). Facial EMG mean frequency decreased (p = 0.01). VAS and HADS scores decreased − 0.76 (p = 0.4) and − 3.375 points (p = 0.37) respectively. Our results indicate parasympathetic system activity, attention shifts, reduced muscle tone, and a relaxed state of mind during MAR. This hints at potential mechanisms of music therapy but needs to be confirmed in larger studies. Electrophysiological changes during music therapy highlight its clinical relevance as a complementary treatment for ICU burn patients.Trial registration: Clinicaltrials.gov (NCT04571255). Registered September 24th, 2020. https//classic.clinicaltrials.gov/ct2/show/NCT04571255.

A Human-Computer Interaction Strategy for An FPGA Platform Boosted Integrated "Perception-Memory" System Based on Electronic Tattoos and Memristors.

The integrated "perception-memory" system is receiving increasing attention due to its crucial applications in humanoid robots, as well as in the simulation of the human retina and brain. Here, a Field Programmable Gate Array (FPGA) platform-boosted system that enables the sensing, recognition, and memory for human-computer interaction is reported by the combination of ultra-thin Ag/Al/Paster-based electronic tattoos (AAP) and Tantalum Oxide/Indium Gallium Zinc Oxide (Ta2O5/IGZO)-based memristors. Notably, the AAP demonstrates exceptional capabilities in accommodating the strain caused by skin deformation, thanks to its unique structural design, which ensures a secure fit to the skin and enables the prolonged monitoring of physiological signals. By utilizing Ta2O5/IGZO as the functional layer, a high switching ratio is conferred to the memristor, and an integrated system for sensing, distinguishing, storing, and controlling the machine hand of multiple human physiological signals is constructed together with the AAP. Further, the proposed system implements emergency calls and smart homes using facial electromyogram signals and utilizing logical entailment to realize the control of the music interface. This innovative "perception-memory" integrated system not only serves the disabled, enhancing human-computer interaction but also provides an alternative avenue to enhance the quality of life and autonomy of individuals with disabilities.

Considering Cuteness Enhances Smiling Responses to Infant Faces1,2

AbstractLaboratory and field research has reported that the appearance of infants causes observers to smile. The current study examined whether this smiling response is modulated by the observer's task and evaluative dimension. Thirty‐nine young nulliparous women were asked to rate the cuteness or beauty levels of 6‐month‐old infants' faces using a 7‐point scale in different blocks. Facial electromyograms (EMGs) were recorded of participants' facial muscles related to both smiling (the zygomaticus major [ZM] and orbicularis oculi [OO]) and frowning (the corrugator supercilii [CS]). The results showed that cuteness and beauty ratings were highly correlated with each other (r = .90), indicating that these evaluations were based on similar attractiveness‐related physical features. Facial EMG responses on the smiling muscle sites, ZM and OO, increased significantly from the baseline when participants rated the faces' cuteness, and the responses were larger than when participants rated the faces' beauty. CS activity was not found to have any effect. The perceived cuteness level of the infant faces did not affect the facial EMG responses. Moreover, the magnitude of the smiling response was shown to be much smaller than that associated with voluntary smiling. These findings suggest that facial expressions while viewing infant faces do not exhibit a fixed pattern but are modulated by observers' tasks and that considering cuteness, which is based on more affective evaluations than beauty, can enhance smiling responses.

Empowering High-Level Spinal Cord Injury Patients in Daily Tasks With a Hybrid Gaze and FEMG-Controlled Assistive Robotic System.

Individuals with high-level spinal cord injuries often face significant challenges in performing essential daily tasks due to their motor impairments. Consequently, the development of reliable, hands-free human-computer interfaces (HCI) for assistive devices is vital for enhancing their quality of life. However, existing methods, including eye-tracking and facial electromyogram (FEMG) control, have demonstrated limitations in stability and efficiency. To address these shortcomings, this paper presents an innovative hybrid control system that seamlessly integrates gaze and FEMG signals. When deployed as a hybrid HCI, this system has been successfully used to assist individuals with high-level spinal cord injuries in performing activities of daily living (ADLs), including tasks like eating, pouring water, and pick-and-place. Importantly, our experimental results confirm that our hybrid control method expedites the performance in pick-place tasks, achieving an average completion time of 34.3 s, which denotes a 28.8% and 21.8% improvement over pure gaze-based control and pure FEMG-based control, respectively. With practice, participants experienced up to a 44% efficiency improvement using the hybrid control method. This state-of-the-art system offers a highly precise and reliable intention interface, suitable for daily use by individuals with high-level spinal cord injuries, ultimately enhancing their quality of life and independence.

Control over emotional facial expressions: Evidence from facial EMG and ERPs in a Stroop-like task

Facial expressions carry important social signals that must be precisely regulated despite potentially conflicting demands on veridicality, communicative intent, and the social situation. In a sample of 19 participants we investigated the challenges of deliberately controlling two facial expressions (smiles and frowns) by the emotional congruency with the expressions of adult and infant counterparts. In a Stroop-like task requiring participants’ deliberate expressions of anger or happiness, we investigated the impact of task-irrelevant background pictures of adults and infants showing negative, neutral, or positive facial expressions. Participants’ deliberate expressions were measured with electromyogram (EMG) of the M. zygomaticus major and M. corrugator supercilii. The latencies of EMG onsets revealed similar congruency effects for smiles and frowns with significant facilitation and inhibition components relative to the neutral condition. Interestingly, the facilitation effect for frown responses by negative facial expressions was significantly smaller vis a vis infant as compared to adult background faces. This diminished facilitation of frowns by infant’s expressions of distress may relate to the activation of caregiver behavior or empathy. We investigated the neural correlates of the observed performance effects by recording event-related-potentials (ERPs). Increased amplitudes in ERP components were observed in incongruent relative to neutral conditions, revealing interference effects on both types of deliberate facial expressions, at different processing stages, namely, structural facial encoding (N170), conflict monitoring (N2), to semantic analysis (N400).

Facial Motion Capture System Based on Facial Electromyogram and Electrooculogram for Immersive Social Virtual Reality Applications.

With the rapid development of virtual reality (VR) technology and the market growth of social network services (SNS), VR-based SNS have been actively developed, in which 3D avatars interact with each other on behalf of the users. To provide the users with more immersive experiences in a metaverse, facial recognition technologies that can reproduce the user's facial gestures on their personal avatar are required. However, it is generally difficult to employ traditional camera-based facial tracking technology to recognize the facial expressions of VR users because a large portion of the user's face is occluded by a VR head-mounted display (HMD). To address this issue, attempts have been made to recognize users' facial expressions based on facial electromyogram (fEMG) recorded around the eyes. fEMG-based facial expression recognition (FER) technology requires only tiny electrodes that can be readily embedded in the HMD pad that is in contact with the user's facial skin. Additionally, electrodes recording fEMG signals can simultaneously acquire electrooculogram (EOG) signals, which can be used to track the user's eyeball movements and detect eye blinks. In this study, we implemented an fEMG- and EOG-based FER system using ten electrodes arranged around the eyes, assuming a commercial VR HMD device. Our FER system could continuously capture various facial motions, including five different lip motions and two different eyebrow motions, from fEMG signals. Unlike previous fEMG-based FER systems that simply classified discrete expressions, with the proposed FER system, natural facial expressions could be continuously projected on the 3D avatar face using machine-learning-based regression with a new concept named the virtual blend shape weight, making it unnecessary to simultaneously record fEMG and camera images for each user. An EOG-based eye tracking system was also implemented for the detection of eye blinks and eye gaze directions using the same electrodes. These two technologies were simultaneously employed to implement a real-time facial motion capture system, which could successfully replicate the user's facial expressions on a realistic avatar face in real time. To the best of our knowledge, the concurrent use of fEMG and EOG for facial motion capture has not been reported before.

Spatio-temporal deep forest for emotion recognition based on facial electromyography signals

Emotion recognition is a key component of human–computer interaction technology, for which facial electromyogram (fEMG) is an important physiological modality. Recently, deep-learning-based emotion recognition using fEMG signals has drawn increased attention. However, the ability of effective feature extraction and the demand of large-scale training data are two dominant factors that restrict the performance of emotion recognition. In this paper, a novel spatio-temporal deep forest (STDF) model is proposed to classify three categories of discrete emotions (neutral, sadness, and fear) using multi-channel fEMG signals.The feature extraction module fully extracts effective spatio-temporal features of fEMG signals using a combination of 2D frame sequences and multi-grained scanning. Meanwhile, a cascade forest-based classifier is designed to provide optimal structures for different scales of training data via automatically adjusting the number of cascade layers.The proposed model and five comparison methods were evaluated on our in-house fEMG dataset that included three discrete emotions and three channels of fEMG electrodes with a total of twenty-seven subjects. Experimental results demonstrate that the proposed STDF model achieves the best recognition performance with an average accuracy of 97.41%. Besides, our proposed STDF model can reduced the scale of training data to 50% while the average accuracy of emotion recognition is only reduced by about 5%. Our proposed model offers an effective solution for practical applications of fEMG-based emotion recognition.

Whose emotion is it? Perspective matters to understand brain-body interactions in emotions

Feeling happy, or judging whether someone else is feeling happy are two distinct facets of emotions that nevertheless rely on similar physiological and neural activity. Differentiating between these two states, also called Self/Other distinction, is an essential aspect of empathy, but how exactly is it implemented? In non-emotional cognition, the transient neural response evoked at each heartbeat, or heartbeat evoked response (HER), indexes the self and signals Self/Other distinction. Here, using electroencephalography (n = 32), we probe whether HERs’ role in Self/Other distinction extends also to emotion – a domain where brain-body interactions are particularly relevant. We asked participants to rate independently validated affective scenes, reporting either their own emotion (Self) or the emotion expressed by people in the scene (Other). During the visual cue indicating to adopt the Self or Other perspective, before the affective scene, HERs distinguished between the two conditions, in visual cortices as well as in the right frontal operculum. Physiological reactivity (facial electromyogram, skin conductance, heart rate) during affective scene co-varied as expected with valence and arousal ratings, but also with the Self- or Other- perspective adopted. Finally, HERs contributed to the subjective experience of valence in the Self condition, in addition to and independently from physiological reactivity. We thus show that HERs represent a trans-domain marker of Self/Other distinction, here specifically contributing to experienced valence. We propose that HERs represent a form of evidence related to the ‘I’ part of the judgement ‘To which extent do I feel happy’. The ‘I’ related evidence would be combined with the affective evidence collected during affective scene presentation, accounting at least partly for the difference between feeling an emotion and identifying it in someone else.

Real-Time Classification of Pain Level Using Zygomaticus and Corrugator EMG Features

The real-time recognition of pain level is required to perform an accurate pain assessment of patients in the intensive care unit, infants, and other subjects who may not be able to communicate verbally or even express the sensation of pain. Facial expression is a key pain-related behavior that may unlock the answer to an objective pain measurement tool. In this work, a machine learning-based pain level classification system using data collected from facial electromyograms (EMG) is presented. The dataset was acquired from part of the BioVid Heat Pain database to evaluate facial expression from an EMG corrugator and EMG zygomaticus and an EMG signal processing and data analysis flow is adapted for continuous pain estimation. The extracted pain-associated facial electromyography (fEMG) features classification is performed by K-nearest neighbor (KNN) by choosing the value of k which depends on the nonlinear models. The presentation of the accuracy estimation is performed, and considerable growth in classification accuracy is noticed when the subject matter from the features is omitted from the analysis. The ML algorithm for the classification of the amount of pain experienced by patients could deliver valuable evidence for health care providers and aid treatment assessment. The proposed classification algorithm has achieved a 99.4% accuracy for classifying the pain tolerance level from the baseline (P0 versus P4) without the influence of a subject bias. Moreover, the result on the classification accuracy clearly shows the relevance of the proposed approach.

Model of Emotion Judgment Based on Features of Multiple Physiological Signals

The model of emotion judgment based on features of multiple physiological signals was investi-gated. In total, 40 volunteers participated in the experiment by playing a computer game while their physiological signals (skin electricity, electrocardiogram (ECG), pulse wave, and facial electromy-ogram (EMG)) were acquired. The volunteers were asked to complete an emotion questionnaire where six typical events that appeared in the game were included, and each volunteer rated their own emotion when experiencing the six events. Based on the analysis of game events, the signal data were cut into segments and the emotional trends were classified. The correlation between data segments and emotional trends was built using a statistical method combined with the questionnaire responses. The set of optimal signal features was obtained by processing the data of physiological signals, extracting the features of signal data, reducing the dimensionality of signal features, and classifying the emotion based on the set of signal data. Finally, the model of emotion judgment was established by selecting the features with a significance of 0.01 based on the correlation between the features in the set of optimal signal features and emotional trends.

Closed-Loop Tracking and Regulation of Emotional Valence State From Facial Electromyogram Measurements.

Affective studies provide essential insights to address emotion recognition and tracking. In traditional open-loop structures, a lack of knowledge about the internal emotional state makes the system incapable of adjusting stimuli parameters and automatically responding to changes in the brain. To address this issue, we propose to use facial electromyogram measurements as biomarkers to infer the internal hidden brain state as feedback to close the loop. In this research, we develop a systematic way to track and control emotional valence, which codes emotions as being pleasant or obstructive. Hence, we conduct a simulation study by modeling and tracking the subject's emotional valence dynamics using state-space approaches. We employ Bayesian filtering to estimate the person-specific model parameters along with the hidden valence state, using continuous and binary features extracted from experimental electromyogram measurements. Moreover, we utilize a mixed-filter estimator to infer the secluded brain state in a real-time simulation environment. We close the loop with a fuzzy logic controller in two categories of regulation: inhibition and excitation. By designing a control action, we aim to automatically reflect any required adjustments within the simulation and reach the desired emotional state levels. Final results demonstrate that, by making use of physiological data, the proposed controller could effectively regulate the estimated valence state. Ultimately, we envision future outcomes of this research to support alternative forms of self-therapy by using wearable machine interface architectures capable of mitigating periods of pervasive emotions and maintaining daily well-being and welfare.

Deep-learning-based real-time silent speech recognition using facial electromyogram recorded around eyes for hands-free interfacing in a virtual reality environment

Deep-learning-based real-time silent speech recognition using facial electromyogram recorded around eyes for hands-free interfacing in a virtual reality environment

Classification of Individual’s discrete emotions reflected in facial microexpressions using electroencephalogram and facial electromyogram

Facial microexpressions are defined as brief, subtle, and involuntary movements of facial muscles reflecting genuine emotions that a person tries to conceal. Because microexpressions are involuntary and uncontrollable, automatic detection of microexpressions and recognition of emotions reflected in the microexpressions can be used in various applications. With the advancement of artificial-intelligence-based non-face-to-face interviews and computer-assisted treatment of mood disorders, the need for developing a technique to precisely detect microexpressions is gradually increasing. In this study, we developed facial electromyography (fEMG)- and electroencephalography (EEG)-based methods for the detection of microexpressions and recognition of emotions reflected in microexpressions as a potential alternative to computer vision-based methods. We first assessed the performance of microexpression detection, and then evaluated the performance of classification of the emotions reflected in the microexpressions. In our experiments with 16 participants, six discrete emotions could be classified using support vector machine with the best F1 score of 0.971 when optimal fEMG and EEG channels were selected, demonstrating the potential usability of the fEMG- and EEG-based emotion recognition method in practical scenarios. It is noteworthy that EEG was more useful for classifying discrete emotions compared to fEMG (best F1 scores: EEG–0.962; fEMG–0.797). To the best of our knowledge, this is the first study to estimate emotions reflected in facial microexpressions using EEG.

Efficacy evaluation and mechanism study of electroacupuncture intervention in acute phase of IFP: study protocol for a randomized controlled trial

BackgroundPrevious studies had already reported the efficacy of electroacupuncture treatment for idiopathic facial nerve palsy (IFP) in a recovery phase; however, the initial use of electroacupuncture in the acute phase remains controversial. Hence, in the present study, we will add electroacupuncture intervention based on oral prednisone tablets in the acute phase of IFP and compare the clinical effects with simple oral prednisone tablets. Besides, the prognosis and safety will be evaluated. The present study aims to evaluate the clinical efficacy, prognosis, and safety of electroacupuncture combined with oral prednisone tablets in the acute phase of IFP, using surface electromyography (sEMG) to objectively observe the recovery of facial expression muscle function. In addition, the morphological changes of the facial nerve were observed dynamically by magnetic resonance imaging (MRI) in the acute phase to reveal the effect mechanism of this therapy.MethodsRandomized controlled trials will be conducted in the first teaching hospital of Tianjin University of Traditional Chinese Medicine in China from September 2020 to April 2021. The treatment will be carried out in two phases, including the acute phase and the recovery phase. Eighty IFP patients will be recruited and randomized into two groups with a 1:1 ratio. Subjects in the acute phases of the control group will be given oral prednisone tablets, based on the control group, and subjects of the experimental group will be given electroacupuncture therapy simultaneously in the acute phase. Both groups will be stopped taking prednisone tablets and turn to electroacupuncture therapy in a recovery phase. Patients in the experimental group will receive treatment at least 6 times in the acute phase and both groups will receive treatment 9 times in the recovery phase, and there will be a follow-up period of 15 days after the treatment. The primary outcome is supposed to be related to the changes observed in the Sunnybrook (Toronto) Facial Grading System (SFGS) on the baseline and day 30 after the onset. Secondary outcome measures will include House-Brackmann Facial Nerve Grading System (H-B), measurement of Numerical Rating Scale (NRS), Facial Disability Index scale (FDI, including physical function subscore, FDIP, and social functioning and well-being subscore, FDIS), surface electromyogram (sEMG), and magnetic resonance imaging (MRI). Adverse events will be recorded for safety assessment.DiscussionThe results of this trial will allow the present study to compare the difference in efficacy and prognosis between the strategy of combined electroacupuncture in the acute phase and only oral prednisone tablets. The findings from this trial will be published in peer-reviewed journals.Trial registrationCHICTR ChiCTR2000038472. Registered on 23 September 2020

Performance enhancement of facial electromyogram-based facial-expression recognition for social virtual reality applications using linear discriminant analysis adaptation.

Recent studies have indicated that facial electromyogram (fEMG)-based facial-expression recognition (FER) systems are promising alternatives to the conventional camera-based FER systems for virtual reality (VR) environments because they are economical, do not depend on the ambient lighting, and can be readily incorporated into existing VR headsets. In our previous study, we applied a Riemannian manifold-based feature extraction approach to fEMG signals recorded around the eyes and demonstrated that 11 facial expressions could be classified with a high accuracy of 85.01%, with only a single training session. However, the performance of the conventional fEMG-based FER system was not high enough to be applied in practical scenarios. In this study, we developed a new method for improving the FER performance by employing linear discriminant analysis (LDA) adaptation with labeled datasets of other users. Our results indicated that the mean classification accuracy could be increased to 89.40% by using the LDA adaptation method (p < .001, Wilcoxon signed-rank test). Additionally, we demonstrated the potential of a user-independent FER system that could classify 11 facial expressions with a classification accuracy of 82.02% without any training sessions. To the best of our knowledge, this was the first study in which the LDA adaptation approach was employed in a cross-subject manner. It is expected that the proposed LDA adaptation approach would be used as an important method to increase the usability of fEMG-based FER systems for social VR applications.

Autonomous Nervous Response During Sedation in Colonoscopy and the Relationship With Clinician Satisfaction.

Background: Nurse assisted propofol sedation (NAPS) is a common method used for colonoscopies. It is safe and widely accepted by patients. Little is known, however, about the satisfaction of clinicians performing colonoscopies with NAPS and the factors that negatively influence this perception such as observer-reported pain events. In this study, we aimed to correlate observer-reported pain events with the clinicians' satisfaction with the procedure. Additionally, we aimed to identify patient biosignals from the autonomic nervous system (B-ANS) during an endoscopy that correlate with those pain events.Methods: Consecutive patients scheduled for a colonoscopy with NAPS were prospectively recruited. During the procedure, observer-reported pain events, which included movements and paralinguistic sounds, were simultaneously recorded with different B-ANS (facial electromyogram (EMG), skin conductance level, body temperature and electrocardiogram). After the procedure, the examiners filled out the Clinician Satisfaction with Sedation Instrument (CSSI). The primary endpoint was the correlation between CSSI and observer-reported pain events. The second primary endpoint was the identification of B-ANS that make it possible to predict those events. Secondary endpoints included the correlation between CSSI and sedation depth, the frequency and dose of sedative use, polyps resected, resection time, the duration of the procedure, the time it took to reach the coecum and the experience of the nurse performing the NAPS. ClinicalTrials.gov: NCT03860779.Results: 112 patients with 98 (88.5%) available B-ANS recordings were prospectively recruited. There was a significant correlation between an increased number of observer-reported pain events during an endoscopy with NAPS and a lower CSSI (r = −0.318, p = 0.001). Additionally, the EMG-signal from facial muscles correlated best with the event time points, and the signal significantly exceeded the baseline 30 s prior to the occurrence of paralinguistic sounds. The secondary endpoints showed that the propofol dose relative to the procedure time, the cecal intubation time, the time spent on polyp removal and the individual nurse performing the NAPS significantly correlated with CSSI.Conclusion: This study shows that movements and paralinguistic sounds during an endoscopy negatively correlate with the satisfaction of the examiner measured with the CSSI. Additionally, an EMG of the facial muscles makes it possible to identify such events and potentially predict their occurrence.

Psychophysiological Modeling of Trust In Technology

Trust as a precursor for users' acceptance of artificial intelligence (AI) technologies that operate as a conceptual extension of humans (e.g., autonomous vehicles (AVs)) is highly influenced by users' risk perception amongst other factors. Prior studies that investigated the interplay between risk and trust perception recommended the development of real-time tools for monitoring cognitive states (e.g., trust). The primary objective of this study was to investigate a feature selection method that yields feature sets that can help develop a highly optimized and stable ensemble trust classifier model. The secondary objective of this study was to investigate how varying levels of risk perception influence users' trust and overall reliance on technology. A within-subject four-condition experiment was implemented with an AV driving game. This experiment involved 25 participants, and their electroencephalogram, electrodermal activity, and facial electromyogram psychophysiological signals were acquired. We applied wrapper, filter, and hybrid feature selection methods on the 82 features extracted from the psychophysiological signals. We trained and tested five voting-based ensemble trust classifier models using training and testing datasets containing only the features identified by the feature selection methods. The results indicate the superiority of the hybrid feature selection method over other methods in terms of model performance. In addition, the self-reported trust measurement and overall reliance of participants on the technology (AV) measured with joystick movements throughout the game reveals that a reduction in risk results in an increase in trust and overall reliance on technology.

Automated affect classification and task difficulty adaptation in a competitive scenario based on physiological linkage: An exploratory study

In competitive and cooperative scenarios, task difficulty should be dynamically adapted to suit people with different abilities. State-of-the-art difficulty adaptation methods for such scenarios are based on task performance, which conveys little information about user-specific factors such as workload. Thus, we present an exploratory study of automated affect recognition and task difficulty adaptation in a competitive scenario based on physiological linkage (covariation of participants’ physiological responses). Classification algorithms were developed in an open-loop study where 16 pairs played a competitive game while 5 physiological responses were measured: respiration, skin conductance, electrocardiogram, and 2 facial electromyograms. Physiological and performance data were used to classify four self-reported variables (enjoyment, valence, arousal, perceived difficulty) into two or three classes. The highest classification accuracies were obtained for perceived difficulty: 84.3% for two-class and 60.5% for three-class classification. As a proof of concept, the developed classifiers were used in a small closed-loop study to dynamically adapt game difficulty. While this closed-loop study found no clear advantages of physiology-based adaptation, it demonstrated the technical feasibility of such real-time adaptation. In the long term, physiology-based task adaptation could enhance competition and cooperation in many multi-user settings (e.g., education, manufacturing, exercise).

Is the pre-operative lateral spread response on facial electromyography a valid diagnostic tool for hemifacial spasm?

The lateral spread response (LSR) on preoperative facial electromyogram (EMG) is a useful tool in evaluating patients with hemifacial spasm (HFS). There may be some instances where the LSR does not appear on the preoperative EMG, thus disrupting the diagnosis and treatment of HFS. In this study, we evaluated the patients who did not exhibit LSR on preoperative EMG but underwent microvascular decompression (MVD) for hemifacial spasm. We searched for patients who underwent MVD for HFS but had an absence of LSR on preoperative EMG between January 2016 and June 2018. Surgical outcomes were evaluated at 1, 3, and 6months after surgery. Follow-up facial EMG was performed 3months after surgery. Results were divided into two categories: (1) spasm relief within 24h of surgery and (2) spasm was observed immediately post-operation. The following parameters were analyzed when comparing between the two groups: age, sex, affected side, duration of symptoms, and offending vessel(s). A total of 306 patients underwent MVD for HFS during the study period. Among them, 13 (4.2%) patients had no LSR on preoperative EMG. Eight patients (61.5%) were female and five patients were male. The 13 patients had a mean age of 51years. All patients exhibited probable offending vessels in the root exit zone (REZ) of the facial nerve on preoperative magnetic resonance (MR) imaging that was confirmed during surgery. Seven patients were free of HFS immediately after surgery, though six patients were not. Only one (7.7%) patient had persisted symptom 6months after surgery. No patients experienced recurrence of spasm, nor exhibited abnormal waves on follow-up facial EMG. LSR on facial EMG is a valuable tool for evaluating hemifacial spasm. However, although LSR did not appear on preoperative EMG, if the patient presents with typical symptoms and the offending vessels are identified on MRI, we expect good results after MVD for HFS.

Efficacy of surgical repair for the functional restoration of injured facial nerve

BackgroundEarly surgical repair to restore nerve integrity has become the most commonly practiced method for managing facial nerve injury. However, the evidence for the efficacy of surgical repair for restoring the function of facial nerves remains deficient. This study evaluated the outcomes of surgical repair for facial nerve lesions.MethodsThis retrospective observational study recruited 28 patients with the diagnosis of facial nerve injury who consecutively underwent surgical repairs from September 2012 to May 2019. All related clinical data were retrospectively analyzed according to age, sex, location of the facial nerve lesion, size of the facial nerve defect, method of repair, facial electromyogram, and blink reflex. Facial function was then stratified with the House-Brackmann grading system pre-operation and 3, 9, 15, and 21 months after surgical repair.ResultsThe 28 patients enrolled in this study included 17 male and 11 female patients with an average age of 34.3 ± 17.4 years. Three methods were applied for the repair of an injured facial nerve, including great auricular nerve transplantation in 15 patients, sural nerve grafting in 7 patients, and hypoglossal to facial nerve anastomosis in 6 patients. Facial nerve function was significantly improved at 21 months after surgery compared with pre-operative function (P = 0.008). Following surgical repair, a correlation was found between the amplitude of motor unit potential (MUP) and facial nerve function (r = -6.078, P = 0.02). Moreover, the extent of functional restoration of the facial nerve at 21 months after surgery depended on the location of the facial nerve lesion; lesions at either the horizontal or vertical segment showed significant improvement(P = 0.008 and 0.005), while no functional restoration was found for lesions at the labyrinthine segment (P = 0.26).ConclusionsFor surgical repair of facial nerve lesions, the sural nerve, great auricular nerve, and hypoglossal-facial nerve can be grafted effectively to store the function of a facial nerve, and MUP may provide an effective indicator for monitoring the recovery of the injured nerve.