Micro-expression recognition based on images 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.

Music style is tightly connected with listeners’ emotional processes and neural activities. However, it remains unclear how the brain works when different music styles are processed emotionally. The current study analyzed the neural activation associated with five music styles during emotion-evoking. Twenty non-musicians participated in the functional magnetic resonance imaging (fMRI) scanning and the emotional ratings of pleasure and arousal evoked by pop, rock, jazz, folk, and classical music. Results showed that classical music was associated with the highest pleasure rating and deactivation of the corpus callosum. Rock music was associated with the highest arousal rating and deactivation of the cingulate gyrus. Pop music activated the bilateral supplementary motor areas (SMA) and the superior temporal gyrus (STG) with moderate pleasure and arousal. As the first fMRI experiment investigating the relationship between the music style and emotion, it provides neural correlates of different music styles during emotion-evoking.

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.

Psychological studies have generally shown that individuals with Autism Spectrum Disorder (ASD) have particularity in the processing of social information by using static or abstract images. Yet, a recent study showed that there was no difference in their use of social or non-social cues in dynamic interactive situations. To establish the cause of the inconsistent results, we added gaze cues in different directions to the chase detection paradigm to explore whether they would affect the performance of participants with ASD. Meanwhile, eye-tracking methodology was used to investigate whether the processing patterns of gaze cues were different between individuals with ASD and TD. In this study, unlike typical controls, participants with ASD showed no detection advantage when the direction of gaze was consistent with the direction of movement (oriented condition). The results suggested that individuals with ASD may utilize an atypical processing pattern, which makes it difficult for them to use social information contained in oriented gaze cues in dynamic interactive situations.

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.

Introduction: Nostalgia is a complex emotional experience involving fond memories of the past and mild sadness, characterized by positive emotions associated with reflecting on previous events. It can awaken emotional memories of loved ones or significant events, contributing to an increase in positive emotions. An unresolved question regarding nostalgia is whether different channels of nostalgia input exhibit distinct mechanisms.Methods: This study examined the emotional and neural effects of nostalgia using various sensory channels through behavioral experiments and electroencephalography (EEG) measurements conducted with college students in China. Participants' emotions were elicited using nostalgic and non-nostalgic stimuli presented through three different sensory channels: auditory (sound only), visual (e.g., still images or synchronized lyrics related to music), and audiovisual (a combination of sound and visual elements, such as music videos).Results: The results demonstrated that nostalgic stimuli elicited significantly higher levels of emotional arousal, pleasure, nostalgia, and dominance compared to non-nostalgic stimuli. At the neural level, nostalgic stimuli enhanced the connection strength, global and local efficiency, and diminished eigenpath length of brain networks in the alpha and gamma bands. Additionally, nostalgia through the auditory channel induced higher activity intensity in the theta and gamma bands and increased brainwave amplitudes in the alpha bands. The audiovisual channel was capable of triggering stronger alpha-wave responses than the visual channel alone.Discussion: These findings suggest that nostalgia effectively triggers positive emotional states and enhances cognitive processing. The audiovisual channel, in particular, showed advantages in eliciting alpha-wave responses. Further research is needed to explore the potential of nostalgia as an adjunctive therapeutic tool.