Facial Electromyography Signals Research Articles

Crosstalk in Facial EMG and Its Reduction Using ICA

There is ample evidence that electromyography (EMG) signals from the corrugator supercilii and zygomatic major muscles can provide valuable information for the assessment of subjective emotional experiences. Although previous research suggested that facial EMG data could be affected by crosstalk from adjacent facial muscles, it remains unproven whether such crosstalk occurs and, if so, how it can be reduced. To investigate this, we instructed participants (n = 29) to perform the facial actions of frowning, smiling, chewing, and speaking, in isolation and combination. During these actions, we measured facial EMG signals from the corrugator supercilii, zygomatic major, masseter, and suprahyoid muscles. We performed an independent component analysis (ICA) of the EMG data and removed crosstalk components. Speaking and chewing induced EMG activity in the masseter and suprahyoid muscles, as well as the zygomatic major muscle. The ICA-reconstructed EMG signals reduced the effects of speaking and chewing on zygomatic major activity, compared with the original signals. These data suggest that: (1) mouth actions could induce crosstalk in zygomatic major EMG signals, and (2) ICA can reduce the effects of such crosstalk.

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.

A Generalized Review Of Human-Computer Interaction Using Electromyogram Signals

Abstract: The primary use of human computer interaction is in smart home as well as in industry 4.0. Communication between computer and human can be benefitted by a spontaneous interchange of emotions. The objective of the work is to provide an idea regarding the process of identifying various emotions using facial electromyography signals through electrode placement method. Here one contemplated the facial electromyography on masticatory function assessment and emotional articulation monitoring. Furthermore, we have also presented the measurement of facial electromyography including selection of electrode, location of electrode and reduction of noise. Facial emotions have significant effect on cognitive process of the human brain such as doubt perception, ability to solve problems, learning capabilities, emotional interactions and memory which is beneficial while interacting with patients suffering from depression and stress. The patients are guided through their rehabilitation process by rehabilitation application while accustoming itself to patient’s emotional state or wellbeing, which results in high motivation as well as in a quicker. This review paper will motivate and inspire researchers and engineers for finding more suitable system for various applications.

Anxiety, not regulation tendency, predicts how individuals regulate in the laboratory: An exploratory comparison of self-report and psychophysiology.

Anxiety influences how individuals experience and regulate emotions in a variety of ways. For example, individuals with lower anxiety tend to cognitively reframe (reappraise) negative emotion and those with higher anxiety tend to suppress negative emotion. Research has also investigated these individual differences with psychophysiology. These lines of research assume coherence between how individuals regulate outside the laboratory, typically measured with self-report, and how they regulate during an experiment. Indeed, performance during experiments is interpreted as an indication of future behavior outside the laboratory, yet this relationship is seldom directly explored. To address this gap, we computed psychophysiological profiles of uninstructed (natural) regulation in the laboratory and explored the coherence between these profiles and a) self-reported anxiety and b) self-reported regulation tendency. Participants viewed negative images and were instructed to reappraise, suppress or naturally engage. Electrodermal and facial electromyography signals were recorded to compute a multivariate psychophysiological profile of regulation. Participants with lower anxiety exhibited similar profiles when naturally regulating and following instructions to reappraise, suggesting they naturally reappraised more. Participants with higher anxiety exhibited similar profiles when naturally regulating and following instructions to suppress, suggesting they naturally suppressed more. However, there was no association between self-reported reappraisal or suppression tendency and psychophysiology. These exploratory results indicate that anxiety, but not regulation tendency, predicts how individuals regulate emotion in the laboratory. These findings suggest that how individuals report regulating in the real world does not map on to how they regulate in the laboratory. Taken together, this underscores the importance of developing emotion-regulation interventions and paradigms that more closely align to and predict real-world outcomes.

Emotional valence sensing using a wearable facial EMG device

Emotion sensing using physiological signals in real-life situations can be practically valuable. Previous studies have developed wearable devices that record autonomic nervous system activity, which reflects emotional arousal. However, no study determined whether emotional valence can be assessed using wearable devices. To this end, we developed a wearable device to record facial electromyography (EMG) from the corrugator supercilii (CS) and zygomatic major (ZM) muscles. To validate the device, in Experiment 1, we used a traditional wired device and our wearable device, to record participants’ facial EMG while they were viewing emotional films. Participants viewed the films again and continuously rated their recalled subjective valence during the first viewing. The facial EMG signals recorded using both wired and wearable devices showed that CS and ZM activities were, respectively, negatively and positively correlated with continuous valence ratings. In Experiment 2, we used the wearable device to record participants’ facial EMG while they were playing Wii Bowling games and assessed their cued-recall continuous valence ratings. CS and ZM activities were correlated negatively and positively, respectively, with continuous valence ratings. These data suggest the possibility that facial EMG signals recorded by a wearable device can be used to assess subjective emotional valence in future naturalistic studies.

Pattern Recognition of Facial Electromyography (FEMG) Signal for Aceh Language Speech using Naïve Bayes and Learning Vector Quantization (LVQ)

Facial electromyography is myoelectric signals that formed by human facial muscles. This signal can be acquired by attaching an electrode to the facial muscle that has been connected with an electromyography sensor. When human say certain words, the articulation muscles contract and facial electromyography signals appear in the muscles. This study aims to recognize patterns in facial electromyography signals by classifying signals using naïve bayes and learning vector quantization classifier. Feature extraction used one-dimensional discrete wavelet transforms. Wavelet transform type used wavelet daubechies2 level 5. The transformation produces a level 5 approximation coefficient called a5 and five detail coefficients called d1, d2, d3, d4, and d5. The result of this study show that the average classification accuracy for ho neuk jak sentence using naïve bayes and LVQ classifier was 62.5% and 92.5% respectively. The average classification accuracy for ja’ word using naïve bayes and LVQ classifier was 70% and 92.5% respectively. The average classification accuracy for ja’ wo sentence using naïve bayes and LVQ classifier was 52.5% and 90% respectively. The average classification accuracy for pane word using naïve bayes and LVQ was 70% and 90% respectively. The average classification accuracy for soe word using naïve bayes and LVQ classifier is 85% and 95% respectively. Thus, this study shows that when humans say the words, facial electromyography signals that appear on facial muscles difference for each subject.

Facial EMG Activity Is Associated with Hedonic Experiences but not Nutritional Values While Viewing Food Images.

The physiological correlates of hedonic/emotional experiences to visual food stimuli are of theoretical and practical interest. Previous psychophysiological studies have shown that facial electromyography (EMG) signals were related to subjective hedonic ratings in response to food images. However, because other data showed positive correlations between hedonic ratings and objective nutritional values of food, whether the facial EMG reactions to food images could reflect the hedonic evaluation or nutritional assessment of food remains unknown. To address this issue, we measured subjective hedonic ratings (liking, wanting, valence, and arousal) and physiological signals (facial EMG of the corrugator supercilii, zygomatic major, masseter, and suprahyoid muscles, skin potential responses, and heart rates) while participants observed food images that had objective nutritional information (caloric, carbohydrate, fat, and protein contents). The results revealed that zygomatic major EMG activity was positively correlated with ratings of liking, wanting, and valence, but not with any objective nutritional value. These data indicate that facial EMG signals in response to food images reflect subjective hedonic experiences, but not objective nutritional values, associated with the food item.

The effect of eye gaze direction on emotional mimicry: A multimodal study with electromyography and electroencephalography

Emotional mimicry plays an important role in social interaction and is influenced by social context, especially eye gaze direction. However, the neural mechanism underlying the effect of eye gaze direction on emotional mimicry is unclear. Here, we explored how eye gaze direction influenced emotional mimicry with a combination of electromyography (EMG) and electroencephalography (EEG) techniques, which may provide a more comprehensive measure. To do this, we recorded facial EMG and scalp EEG signals simultaneously while participants observed emotional faces (happy vs. angry) with direct or averted gaze. Then, we split the EEG trials into two mimicry intensity categories (high mimicry intensity, HMI vs. low mimicry intensity, LMI) according to EMG activity. The ERP difference between HMI and LMI EEG trials revealed four ERP components (P50, P150, N200 and P300), and the effect of eye gaze direction on emotional mimicry was prominent on P300 at P7 and P8. Moreover, we also observed differences in the effect of eye gaze direction on mimicry of happy faces and angry faces, which were found on P300 at P7, as well as P150 at P7 and N200 at P7 and Pz. In short, the present study isolated the neural signals of emotional mimicry with a new multimodal method, and provided empirical neural evidence that eye gaze direction affected emotional mimicry.

Facial EMG Correlates of Subjective Hedonic Responses During Food Consumption.

An exploration of physiological correlates of subjective hedonic responses while eating food has practical and theoretical significance. Previous psychophysiological studies have suggested that some physiological measures, including facial electromyography (EMG), may correspond to hedonic responses while viewing food images or drinking liquids. However, whether consuming solid food could produce such subjective–physiological concordance remains untested. To investigate this issue, we assessed participants’ subjective ratings of liking, wanting, valence, and arousal while they consumed gel-type food stimuli of various flavors and textures. We additionally measured their physiological signals, including facial EMG from the corrugator supercilii. The results showed that liking, wanting, and valence ratings were negatively correlated with corrugator supercilii EMG activity. Only the liking rating maintained a negative association with corrugator supercilii activity when the other ratings were partialed out. These data suggest that the subjective hedonic experience, specifically the liking state, during food consumption can be objectively assessed using facial EMG signals and may be influenced by such somatic signals.

Continuous electromyography monitoring of motor cranial nerves during cerebellopontine angle surgery.

Electromyography (EMG) monitoring is expected to reduce the incidence of motor cranial nerve deficits in cerebellopontine angle surgery. The aim of this study was to provide a detailed analysis of intraoperative EMG phenomena with respect to their surgical significance. Using a system that continuously records facial and lower cranial nerve EMG signals during the entire operative procedure, the authors examined 30 patients undergoing surgery on acoustic neuroma (24 patients) or meningioma (six patients). Free-running EMG signals were recorded from muscles targeted by the facial, trigeminal, and lower cranial nerves, and were analyzed off-line with respect to waveform characteristics, frequencies, and amplitudes. Intraoperative measurements were correlated with typical surgical maneuvers and postoperative outcomes. Characteristic EMG discharges were obtained: spikes and bursts were recorded immediately following the direct manipulation of a dissecting instrument near the cranial nerve, but also during periods when the nerve had not yet been exposed. Bursts could be precisely attributed to contact activity. Three distinct types of trains were identified: A, B, and C trains. Whereas B and C trains are irrelevant with respect to postoperative outcome, the A train--a sinusoidal, symmetrical sequence of high-frequency and low-amplitude signals--was observed in 19 patients and could be well correlated with additional postoperative facial nerve paresis (in 18 patients). It could be demonstrated that the occurrence of A trains is a highly reliable predictor for postoperative facial palsy. Although some degree of functional worsening is to be expected postoperatively, there is a good chance of avoiding major deficits by warning the surgeon early. Continuous EMG monitoring is superior to electrical nerve stimulation or acoustic loudspeaker monitoring alone. The detailed analysis of EMG-waveform characteristics is able to provide more accurate warning criteria during surgery.