Emotion classification and recognition is a crucial area in emotional computing. Physiological signals, such as electroencephalogram (EEG), provide an accurate reflection of emotions and are difficult to disguise. However, emotion recognition still faces challenges in single-modal signal feature extraction and multi-modal signal integration. This study collected EEG, electromyogram (EMG), and electrodermal activity (EDA) signals from participants under three emotional states: happiness, sadness, and fear. A feature-weighted fusion method was applied for integrating the signals, and both support vector machine (SVM) and extreme learning machine (ELM) were used for classification. The results showed that the classification accuracy was highest when the fusion weights were set to EEG 0.7, EMG 0.15, and EDA 0.15, achieving accuracy rates of 80.19% and 82.48% for SVM and ELM, respectively. These rates represented an improvement of 5.81% and 2.95% compared to using EEG alone. This study offers methodological support for emotion classification and recognition using multi-modal physiological signals.
Humans ; Emotions/physiology* ; Electroencephalography ; Support Vector Machine ; Electromyography ; Signal Processing, Computer-Assisted ; Galvanic Skin Response/physiology* ; Machine Learning ; Male

Existing emotion recognition research is typically limited to static laboratory settings and has not fully handle the changes in emotional states in dynamic scenarios. To address this problem, this paper proposes a method for dynamic continuous emotion recognition based on electroencephalography (EEG) and eye movement signals. Firstly, an experimental paradigm was designed to cover six dynamic emotion transition scenarios including happy to calm, calm to happy, sad to calm, calm to sad, nervous to calm, and calm to nervous. EEG and eye movement data were collected simultaneously from 20 subjects to fill the gap in current multimodal dynamic continuous emotion datasets. In the valence-arousal two-dimensional space, emotion ratings for stimulus videos were performed every five seconds on a scale of 1 to 9, and dynamic continuous emotion labels were normalized. Subsequently, frequency band features were extracted from the preprocessed EEG and eye movement data. A cascade feature fusion approach was used to effectively combine EEG and eye movement features, generating an information-rich multimodal feature vector. This feature vector was input into four regression models including support vector regression with radial basis function kernel, decision tree, random forest, and K-nearest neighbors, to develop the dynamic continuous emotion recognition model. The results showed that the proposed method achieved the lowest mean square error for valence and arousal across the six dynamic continuous emotions. This approach can accurately recognize various emotion transitions in dynamic situations, offering higher accuracy and robustness compared to using either EEG or eye movement signals alone, making it well-suited for practical applications.
Humans ; Electroencephalography/methods* ; Emotions/physiology* ; Eye Movements/physiology* ; Signal Processing, Computer-Assisted ; Support Vector Machine ; Algorithms

Post-stroke aphasia is associated with a significantly elevated risk of depression, yet the underlying mechanisms remain unclear. This study recorded 64-channel electroencephalogram data and depression scale scores from 12 aphasic patients with depression, 8 aphasic patients without depression, and 12 healthy controls during resting state and an emotional Stroop task. Spectral and microstate analyses were conducted to examine brain activity patterns across conditions. Results showed that depression scores significantly negatively explained the occurrence of microstate class C and positively explained the transition probability from microstate class A to B. Furthermore, aphasic patients with depression exhibited increased alpha-band activation in the frontal region. These findings suggest distinct neural features in aphasic patients with depression and offer new insights into the mechanisms contributing to their heightened vulnerability to depression.
Humans ; Electroencephalography ; Aphasia/etiology* ; Stroop Test ; Emotions/physiology* ; Depression/etiology* ; Male ; Female ; Middle Aged ; Stroke/complications* ; Brain/physiopathology* ; Aged ; Adult ; Rest/physiology*

Current studies on electroencephalogram (EEG) emotion recognition primarily concentrate on discrete stimulus paradigms under controlled laboratory settings, which cannot adequately represent the dynamic transition characteristics of emotional states during multi-context interactions. To address this issue, this paper proposes a novel method for emotion transition recognition that leverages a cross-modal feature fusion and global perception network (CFGPN). Firstly, an experimental paradigm encompassing six types of emotion transition scenarios was designed, and EEG and eye movement data were simultaneously collected from 20 participants, each annotated with dynamic continuous emotion labels. Subsequently, deep canonical correlation analysis integrated with a cross-modal attention mechanism was employed to fuse features from EEG and eye movement signals, resulting in multimodal feature vectors enriched with highly discriminative emotional information. These vectors are then input into a parallel hybrid architecture that combines convolutional neural networks (CNNs) and Transformers. The CNN is employed to capture local time-series features, whereas the Transformer leverages its robust global perception capabilities to effectively model long-range temporal dependencies, enabling accurate dynamic emotion transition recognition. The results demonstrate that the proposed method achieves the lowest mean square error in both valence and arousal recognition tasks on the dynamic emotion transition dataset and a classic multimodal emotion dataset. It exhibits superior recognition accuracy and stability when compared with five existing unimodal and six multimodal deep learning models. The approach enhances both adaptability and robustness in recognizing emotional state transitions in real-world scenarios, showing promising potential for applications in the field of biomedical engineering.
Humans ; Emotions/physiology* ; Electroencephalography ; Neural Networks, Computer ; Eye Movements ; Perception

Premenstrual dysphoric disorder (PMDD) is considered a severe form of premenstrual syndrome (PMS). As a key brain region involved in emotional regulation and stress responses, the amygdala has been implicated in the pathogenesis of PMS/PMDD. The amygdala is composed of multiple subregions, each playing distinct roles in emotion, memory, and stress responses, and forms complex brain areas. Summarizing the interconnections among amygdala, subregions and their connectivity with external areas, and exploringt the neuroimaging characteristics of the amygdala, as well as changes in its neural circuits and brain networks in these patients, will help provide a theoretical foundation for targeted modulation of amygdala function in the treatment of PMS/PMDD.
Humans ; Amygdala/diagnostic imaging* ; Female ; Premenstrual Dysphoric Disorder/pathology* ; Premenstrual Syndrome/pathology* ; Emotions/physiology* ; Magnetic Resonance Imaging

Within the prefrontal-cingulate cortex, abnormalities in coupling between neuronal networks can disturb the emotion-cognition interactions, contributing to the development of mental disorders such as depression. Despite this understanding, the neural circuit mechanisms underlying this phenomenon remain elusive. In this study, we present a biophysical computational model encompassing three crucial regions, including the dorsolateral prefrontal cortex, subgenual anterior cingulate cortex, and ventromedial prefrontal cortex. The objective is to investigate the role of coupling relationships within the prefrontal-cingulate cortex networks in balancing emotions and cognitive processes. The numerical results confirm that coupled weights play a crucial role in the balance of emotional cognitive networks. Furthermore, our model predicts the pathogenic mechanism of depression resulting from abnormalities in the subgenual cortex, and network functionality was restored through intervention in the dorsolateral prefrontal cortex. This study utilizes computational modeling techniques to provide an insight explanation for the diagnosis and treatment of depression.
Prefrontal Cortex/physiology* ; Humans ; Emotions/physiology* ; Cognition/physiology* ; Gyrus Cinguli/physiology* ; Computer Simulation ; Models, Neurological ; Neural Pathways/physiology* ; Nerve Net/physiology*

In modern society, people are increasingly exposed to chronic stress, leading to various mental disorders. However, the activities of brain regions, especially neural firing patterns related to specific behaviors, remain unclear. In this study, we introduce a novel approach, NeuroSync, which integrates open-field behavioral testing with electrophysiological recordings from emotion-related brain regions, specifically the central amygdala and the paraventricular nucleus of the hypothalamus, to explore the mechanisms of negative emotions induced by chronic stress in mice. By applying machine vision techniques, we quantified behaviors in the open field, and signal processing algorithms elucidated the neural underpinnings of the observed behaviors. Synchronizing behavioral and electrophysiological data revealed significant correlations between neural firing patterns and stress-related behaviors, providing insights into real-time brain activity underlying behavioral responses. This research combines deep learning and machine learning to synchronize high-resolution video and electrophysiological data, offering new insights into neural-behavioral dynamics under chronic stress conditions.
Animals ; Mice ; Male ; Emotions/physiology* ; Stress, Psychological/physiopathology* ; Action Potentials/physiology* ; Mice, Inbred C57BL ; Behavior, Animal/physiology* ; Machine Learning ; Amygdala/physiopathology* ; Neurons/physiology* ; Paraventricular Hypothalamic Nucleus/physiopathology* ; Brain/physiology*

The insula is a complex brain region central to the orchestration of taste perception, interoception, emotion, and decision-making. Recent research has shed light on the intricate connections between the insula and other brain regions, revealing the crucial role of this area in integrating sensory, emotional, and cognitive information. The unique anatomical position and extensive connectivity allow the insula to serve as a critical hub in the functional network of the brain. We summarize its role in interoceptive and exteroceptive sensory processing, illustrating insular function as a bridge connecting internal and external experiences. Drawing on recent research, we delineate the insular involvement in emotional processes, highlighting its implications in psychiatric conditions, such as anxiety, depression, and addiction. We further discuss the insular contributions to cognition, focusing on its significant roles in time perception and decision-making. Collectively, the evidence underscores the insular function as a dynamic interface that synthesizes diverse inputs into coherent subjective experiences and decision-making processes. Through this review, we hope to highlight the importance of the insula as an interface between sensation, emotion, and cognition, and to inspire further research into this fascinating brain region.
Humans ; Emotions/physiology* ; Cognition/physiology* ; Insular Cortex/physiology* ; Sensation/physiology* ; Animals ; Interoception/physiology* ; Cerebral Cortex/physiology* ; Decision Making/physiology*

Evidence suggests that explicit reappraisal has limited regulatory effects on high-intensity emotions, mainly due to the depletion of cognitive resources occupied by the high-intensity emotional stimulus itself. The implicit form of reappraisal has proved to be resource-saving and therefore might be an ideal strategy to achieve the desired regulatory effect in high-intensity situations. In this study, we explored the regulatory effect of explicit and implicit reappraisal when participants encountered low- and high-intensity negative images. The subjective emotional rating indicated that both explicit and implicit reappraisal down-regulated negative experiences, irrespective of intensity. However, the amplitude of the parietal late positive potential (LPP; a neural index of experienced emotional intensity) showed that only implicit reappraisal had significant regulatory effects in the high-intensity context, though both explicit and implicit reappraisal successfully reduced the emotional neural responses elicited by low-intensity negative images. Meanwhile, implicit reappraisal led to a smaller frontal LPP amplitude (an index of cognitive cost) compared to explicit reappraisal, indicating that the implementation of implicit reappraisal consumes limited cognitive control resources. Furthermore, we found a prolonged effect of implicit emotion regulation introduced by training procedures. Taken together, these findings not only reveal that implicit reappraisal is suitable to relieve high-intensity negative experiences as well as neural responses, but also highlight the potential benefit of trained implicit regulation in clinical populations whose frontal control resources are limited.
Humans ; Emotional Regulation ; Electroencephalography ; Evoked Potentials/physiology* ; Cognition/physiology* ; Emotions/physiology*

Major depressive disorder (MDD) is characterized by emotion dysregulation. Whether implicit emotion regulation can compensate for this deficit remains unknown. In this study, we recruited 159 subjects who were healthy controls, had subclinical depression, or had MDD, and examined them under baseline, implicit, and explicit reappraisal conditions. Explicit reappraisal led to the most negative feelings and the largest parietal late positive potential (parietal LPP, an index of emotion intensity) in the MDD group compared to the other two groups; the group difference was absent under the other two conditions. MDD patients showed larger regulatory effects in the LPP during implicit than explicit reappraisal, whereas healthy controls showed a reversed pattern. Furthermore, the frontal P3, an index of voluntary cognitive control, showed larger amplitudes in explicit reappraisal compared to baseline in the healthy and subclinical groups, but not in the MDD group, while implicit reappraisal did not increase P3 across groups. These findings suggest that implicit reappraisal is beneficial for clinical depression.
Humans ; Depressive Disorder, Major/psychology* ; Emotional Regulation ; Depression ; Emotions/physiology* ; Cognition/physiology*