Micro-Expression Recognition Based on Nodal Efficiency in the EEG Functional Networks.

Micro-expression recognition based on ima- ges has made some progress, yet limitations persist. For instance, image-based recognition of micro-expressions is affected by factors such as ambient light, changes in head posture, and facial occlusion. The high temporal resolution of electroencephalogram (EEG) technology can record brain activity associated with micro-expressions and identify them objectively from a neurophysiological standpoint. Accordingly, this study introduces a novel method for recognizing micro-expressions using node efficiency features of brain networks derived from EEG signals. We designed a real-time Supervision and Emotional Expression Suppression (SEES) experimental paradigm to collect video and EEG data reflecting micro- and macro-expression states from 70 participants experiencing positive emotions. By constructing functional brain networks based on graph theory, we analyzed the network efficiencies at both macro- and micro-levels. The participants exhibited lower connection density, global efficiency, and nodal efficiency in the alpha, beta, and gamma networks during micro-expressions compared to macro-expressions. We then selected the optimal subset of nodal efficiency features using a random forest algorithm and applied them to various classifiers, including Support Vector Machine (SVM), Gradient-Boosted Decision Tree (GBDT), Logistic Regression (LR), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). These classifiers achieved promising accuracy in micro-expression recognition, with SVM exhibiting the highest accuracy of 92.6% when 15 channels were selected. This study provides a new neuroscientific indicator for recognizing micro-expressions based on EEG signals, thereby broadening the potential applications for micro-expression recognition.

Affection of facial artifacts caused by micro-expressions on electroencephalography signals.

Macro-expressions are widely used in emotion recognition based on electroencephalography (EEG) because of their use as an intuitive external expression. Similarly, micro-expressions, as suppressed and brief emotional expressions, can also reflect a person's genuine emotional state. Therefore, researchers have started to focus on emotion recognition studies based on micro-expressions and EEG. However, compared to the effect of artifacts generated by macro-expressions on the EEG signal, it is not clear how artifacts generated by micro-expressions affect EEG signals. In this study, we investigated the effects of facial muscle activity caused by micro-expressions in positive emotions on EEG signals. We recorded the participants' facial expression images and EEG signals while they watched positive emotion-inducing videos. We then divided the 13 facial regions and extracted the main directional mean optical flow features as facial micro-expression image features, and the power spectral densities of theta, alpha, beta, and gamma frequency bands as EEG features. Multiple linear regression and Granger causality test analyses were used to determine the extent of the effect of facial muscle activity artifacts on EEG signals. The results showed that the average percentage of EEG signals affected by muscle artifacts caused by micro-expressions was 11.5%, with the frontal and temporal regions being significantly affected. After removing the artifacts from the EEG signal, the average percentage of the affected EEG signal dropped to 3.7%. To the best of our knowledge, this is the first study to investigate the affection of facial artifacts caused by micro-expressions on EEG signals.

Responses of functional brain networks in micro-expressions: An EEG study.

Micro-expressions (MEs) can reflect an individual's subjective emotions and true mental state, and they are widely used in the fields of mental health, justice, law enforcement, intelligence, and security. However, one of the major challenges of working with MEs is that their neural mechanism is not entirely understood. To the best of our knowledge, the present study is the first to use electroencephalography (EEG) to investigate the reorganizations of functional brain networks involved in MEs. We aimed to reveal the underlying neural mechanisms that can provide electrophysiological indicators for ME recognition. A real-time supervision and emotional expression suppression experimental paradigm was designed to collect video and EEG data of MEs and no expressions (NEs) of 70 participants expressing positive emotions. Based on the graph theory, we analyzed the efficiency of functional brain network at the scalp level on both macro and micro scales. The results revealed that in the presence of MEs compared with NEs, the participants exhibited higher global efficiency and nodal efficiency in the frontal, occipital, and temporal regions. Additionally, using the random forest algorithm to select a subset of functional connectivity features as input, the support vector machine classifier achieved a classification accuracy for MEs and NEs of 0.81, with an area under the curve of 0.85. This finding demonstrates the possibility of using EEG to recognize MEs, with a wide range of application scenarios, such as persons wearing face masks or patients with expression disorders.

Electrophysiological evidence for inhibition hypothesis of micro-expressions based on tensor component analysis and Physarum network algorithm.

The inhibition hypothesis advocated by Ekman (1985) states when an emotion is concealed or masked, the true emotion is manifested as a micro-expression (ME) which is a fleeting expression lasting for 40 to 500 ms. However, research about the inhibition hypothesis of ME from the perspective of electrophysiology is lacking. Here, we report the electrophysiological evidence obtained from an electroencephalography (EEG) data analysis method. Specifically, we designed an ME elicitation paradigm to collect data of MEs of positive emotions and EEG from 70 subjects, and proposed a method based on tensor component analysis (TCA) combined with the Physarum network (PN) algorithm to characterize the spatial, temporal, and spectral signatures of dynamic EEG data of MEs. The proposed TCA-PN methods revealed two pathways involving dorsal and ventral streams in functional brain networks of MEs, which reflected the inhibition processing and emotion arousal of MEs. The results provide evidence for the inhibition hypothesis from an electrophysiological standpoint, which allows us to better understand the neural mechanism of MEs.

Differences in brain activations between micro- and macro-expressions based on electroencephalography.

Micro-expressions can reflect an individual's subjective emotions and true mental state and are widely used in the fields of mental health, justice, law enforcement, intelligence, and security. However, the current approach based on image and expert assessment-based micro-expression recognition technology has limitations such as limited application scenarios and time consumption. Therefore, to overcome these limitations, this study is the first to explore the brain mechanisms of micro-expressions and their differences from macro-expressions from a neuroscientific perspective. This can be a foundation for micro-expression recognition based on EEG signals. We designed a real-time supervision and emotional expression suppression (SEES) experimental paradigm to synchronously collect facial expressions and electroencephalograms. Electroencephalogram signals were analyzed at the scalp and source levels to determine the temporal and spatial neural patterns of micro- and macro-expressions. We found that micro-expressions were more strongly activated in the premotor cortex, supplementary motor cortex, and middle frontal gyrus in frontal regions under positive emotions than macro-expressions. Under negative emotions, micro-expressions were more weakly activated in the somatosensory cortex and corneal gyrus regions than macro-expressions. The activation of the right temporoparietal junction (rTPJ) was stronger in micro-expressions under positive than negative emotions. The reason for this difference is that the pathways of facial control are different; the production of micro-expressions under positive emotion is dependent on the control of the face, while micro-expressions under negative emotions are more dependent on the intensity of the emotion.

Development of an efficient iterative genome editing method in Bacillus subtilis using the CRISPR-AsCpf1 system.

Bacillus subtilis is a useful chassis in the fields of synthetic biology and metabolic engineering for chemical production. Here, we constructed CRISPR-AsCpf1-based expression plasmids with the temperature-sensitive replicon for iterative genome editing in B. subtilis. This method allowed gene insertion and large genomic deletion with an editing efficiency of up 80%-100% and rapid plasmid curing to facilitate the iterative genome editing in B. subtilis 168. Using the customized CRISPR-AsCpf1 system, we successfully and efficiently implemented the related gene editing in B. subtilis 168 for hyaluronic acid (HA) biosynthesis, HA synthase gene (hasA) insertion, UDP-glucose-dehydrogenase gene (tuaD) insertion, and eps gene cluster (epsA-O) deletion. The heterologous production of HA was realized by the engineered strain with a yield of 1.39 g/L. These results support the finding that the CRISPR-AsCpf1 system is highly efficient in bacteria genome editing and provide valuable guidance and essential references for genome engineering in B. subtilis using the CRISPR-AsCpf1 system.

Dynamic Functional Network Connectivity Associated with Musical Emotions Evoked by Different Tempi.

Introduction: Music tempo has strong clinical maneuverability and positive emotional effect in music therapy, which can directly evoke multiple emotions and dynamic neural changes in the whole brain. However, the precise relationship between music tempo and its emotional effects remains unclear. The present study aimed to investigate the dynamic network connectivity (dFNC) associated with emotions elicited by music at different tempi. Materials and Methods: We obtained emotion ratings of fast-tempo (155-170 beats per minute [bpm]), middle-tempo (90 bpm), and slow-tempo (50-60 bpm) piano music from 40 participants both during and after functional magnetic resonance imaging (fMRI). Group independent component analysis (ICA), sliding time window correlations, and k-means clustering were used to assess the dFNC of fMRI data. Paired t-tests were conducted to compare the difference of neural networks. Results: (1) Fast music was associated with higher ratings of emotional valence and arousal, which were accompanied with increasing dFNC between somatomotor (SM) and cingulo-opercular (CO) networks and decreasing dFNC between frontoparietal and SM networks. (2) Even with stronger activation in auditory (AUD) networks, slow music was associated with weaker emotion than fast music, with decreasing functional network connectivity across the brain and the participation of default mode (DM). (3) Middle-tempo music elicited moderate emotional activation with the most stable dFNC in the whole brain. Conclusion: Faster music increases neural activity in the SM and CO regions, increasing the intensity of the emotional experience. In contrast, slower music was associated with decreasing engagement of AUD and stable engagement of DM, resulting in a weak emotional experience. These findings suggested that the time-varying aspects of functional connectivity can help to uncover the dynamic neural substrates of tempo-evoked emotion while listening to music.

Enhancement of riboflavin production in Bacillus subtilis via in vitro and in vivo metabolic engineering of pentose phosphate pathway.

OBJECTIVES: The production of riboflavin with Bacillus subtilis, is an established process, however it is yet to be fully optimized. The aim of this study was to explore how riboflavin yields can be improved via in vitro and in vivo metabolic engineering modification of the pentose phosphate pathway (PPP). RESULTS: In vitro, glucose was replaced with sodium gluconate to enhance PPP. Flask tests showed that the riboflavin titer increased from 0.64 to 0.87 g/L. The results revealed that the direct use of sodium gluconate could benefit riboflavin production. In vivo, gntP (encoding gluconate permease) was overexpressed to improve sodium gluconate uptake. The riboflavin titer reached 1.00 g/L with the mutant B. subtilis RF01. Ultimately, the fermentation verification of the engineered strain was carried out in a 7-L fermenter, with the increased riboflavin titer validating this approach. CONCLUSIONS: The combination of metabolic engineering modifications in vitro and in vivo was confirmed to promote riboflavin production efficiently by increasing PPP and has great potential for industrial application. This work is aimed to explore how to improve the riboflavin yield by the rational renovation of the pentose phosphate pathway (PPP). In vitro, metabolic engineering mainly uses sodium gluconate as a carbon source instead of glucose, and in vivo, metabolic engineering mainly includes the overexpression of sodium gluconate utility-related genes. The effect of sodium gluconate on cell growth, riboflavin production was investigated in the flasks and fermenter scale.

Spatial Connectivity and Temporal Dynamic Functional Network Connectivity of Musical Emotions Evoked by Dynamically Changing Tempo.

Music tempo is closely connected to listeners' musical emotion and multifunctional neural activities. Music with increasing tempo evokes higher emotional responses and music with decreasing tempo enhances relaxation. However, the neural substrate of emotion evoked by dynamically changing tempo is still unclear. To investigate the spatial connectivity and temporal dynamic functional network connectivity (dFNC) of musical emotion evoked by dynamically changing tempo, we collected dynamic emotional ratings and conducted group independent component analysis (ICA), sliding time window correlations, and k-means clustering to assess the FNC of emotion evoked by music with decreasing tempo (180-65 bpm) and increasing tempo (60-180 bpm). Music with decreasing tempo (with more stable dynamic valences) evoked higher valence than increasing tempo both with stronger independent components (ICs) in the default mode network (DMN) and sensorimotor network (SMN). The dFNC analysis showed that with time-decreasing FNC across the whole brain, emotion evoked by decreasing music was associated with strong spatial connectivity within the DMN and SMN. Meanwhile, it was associated with strong FNC between the DMN-frontoparietal network (FPN) and DMN-cingulate-opercular network (CON). The paired t-test showed that music with a decreasing tempo evokes stronger activation of ICs within DMN and SMN than that with an increasing tempo, which indicated that faster music is more likely to enhance listeners' emotions with multifunctional brain activities even when the tempo is slowing down. With increasing FNC across the whole brain, music with an increasing tempo was associated with strong connectivity within FPN; time-decreasing connectivity was found within CON, SMN, VIS, and between CON and SMN, which explained its unstable valence during the dynamic valence rating. Overall, the FNC can help uncover the spatial and temporal neural substrates of musical emotions evoked by dynamically changing tempi.

Multi-method Fusion of Cross-Subject Emotion Recognition Based on High-Dimensional EEG Features.

Emotion recognition using electroencephalogram (EEG) signals has attracted significant research attention. However, it is difficult to improve the emotional recognition effect across subjects. In response to this difficulty, in this study, multiple features were extracted for the formation of high-dimensional features. Based on the high-dimensional features, an effective method for cross-subject emotion recognition was then developed, which integrated the significance test/sequential backward selection and the support vector machine (ST-SBSSVM). The effectiveness of the ST-SBSSVM was validated on a dataset for emotion analysis using physiological signals (DEAP) and the SJTU Emotion EEG Dataset (SEED). With respect to high-dimensional features, the ST-SBSSVM average improved the accuracy of cross-subject emotion recognition by 12.4% on the DEAP and 26.5% on the SEED when compared with common emotion recognition methods. The recognition accuracy obtained using ST-SBSSVM was as high as that obtained using sequential backward selection (SBS) on the DEAP dataset. However, on the SEED dataset, the recognition accuracy increased by ~6% using ST-SBSSVM from that using the SBS. Using the ST-SBSSVM, ~97% (DEAP) and 91% (SEED) of the program runtime was eliminated when compared with the SBS. Compared with recent similar works, the method developed in this study for emotion recognition across all subjects was found to be effective, and its accuracy was 72% (DEAP) and 89% (SEED).

DC Shifts-fMRI: A Supplement to Event-Related fMRI.

Event-related fMRI have been widely used in locating brain regions which respond to specific tasks. However, activities of brain regions which modulate or indirectly participate in the response to a specific task are not event-related. Event-related fMRI can't locate these regulatory regions, detrimental to the integrity of the result that event-related fMRI revealed. Direct-current EEG shifts (DC shifts) have been found linked to the inner brain activity, a fusion DC shifts-fMRI method may have the ability to reveal a more complete response of the brain. In this study, we used DC shifts-fMRI to verify that even when responding to a very simple task, (1) The response of the brain is more complicated than event-related fMRI generally revealed and (2) DC shifts-fMRI have the ability of revealing brain regions whose responses are not in event-related way. We used a classical and simple paradigm which is often used in auditory cortex tonotopic mapping. Data were recorded from 50 subjects (25 male, 25 female) who were presented with randomly presented pure tone sequences with six different frequencies (200, 400, 800, 1,600, 3,200, 6,400 Hz). Our traditional fMRI results are consistent with previous findings that the activations are concentrated on the auditory cortex. Our DC shifts-fMRI results showed that the cingulate-caudate-thalamus network which underpins sustained attention is positively activated while the dorsal attention network and the right middle frontal gyrus which underpin attention orientation are negatively activated. The regional-specific correlations between DC shifts and brain networks indicate the complexity of the response of the brain even to a simple task and that the DC shifts can effectively reflect these non-event-related inner brain activities.

Single-Trial EEG-fMRI Reveals the Generation Process of the Mismatch Negativity.

Although research on the mismatch negativity (MMN) has been ongoing for 40 years, the generation process of the MMN remains largely unknown. In this study, we used a single-trial electro-encephalography (EEG)-functional magnetic resonance imaging (fMRI) coupling method which can analyze neural activity with both high temporal and high spatial resolution and thus assess the generation process of the MMN. We elicited the MMN with an auditory oddball paradigm while recording simultaneous EEG and fMRI. We divided the MMN into five equal-durational phases. Utilizing the single-trial variability of the MMN, we analyzed the neural generators of the five phases, thereby determining the spatiotemporal generation process of the MMN. We found two distinct bottom-up prediction error propagations: first from the auditory cortex to the motor areas and then from the auditory cortex to the inferior frontal gyrus (IFG). Our results support the regularity-violation hypothesis of MMN generation.

Effects of Musical Tempo on Musicians' and Non-musicians' Emotional Experience When Listening to Music.

Tempo is an important musical element that affects human's emotional processes when listening to music. However, it remains unclear how tempo and training affect individuals' emotional experience of music. To explore the neural underpinnings of the effects of tempo on music-evoked emotion, music with fast, medium, and slow tempi were collected to compare differences in emotional responses using functional magnetic resonance imaging (fMRI) of neural activity between musicians and non-musicians. Behaviorally, musicians perceived higher valence in fast music than did non-musicians. The main effects of musicians and non-musicians and tempo were significant, and a near significant interaction between group and tempo was found. In the arousal dimension, the mean score of medium-tempo music was the highest among the three kinds; in the valence dimension, the mean scores decreased in order from fast music, medium music, to slow music. Functional analyses revealed that the neural activation of musicians was stronger than those of non-musicians in the left inferior parietal lobe (IPL). A comparison of tempi showed a stronger activation from fast music than slow music in the bilateral superior temporal gyrus (STG), which provided corresponding neural evidence for the highest valence reported by participants for fast music. Medium music showed stronger activation than slow music in the right Heschl's gyrus (HG), right middle temporal gyrus (MTG), right posterior cingulate cortex (PCC), right precuneus, right IPL, and left STG. Importantly, this study confirmed and explained the connection between music tempo and emotional experiences, and their interaction with individuals' musical training.