Investigating EEG-based functional connectivity patterns for multimodal emotion recognition

An Efficient Graph Learning System for Emotion Recognition Inspired by the Cognitive Prior Graph of EEG Brain Network

Benefiting from the high-temporal resolution of electroencephalogram (EEG), EEG-based emotion recognition has become one of the hotspots of affective computing. For EEG-based emotion recognition systems, it is crucial to utilize state-of-the-art learning strategies to automatically learn emotion-related brain cognitive patterns from emotional EEG signals, and the learned stable cognitive patterns effectively ensure the robustness of the emotion recognition system. In this work, to realize the efficient decoding of emotional EEG, we propose a graph learning system [Graph Convolutional Network framework with Brain network initial inspiration and Fused attention mechanism (BF-GCN)] inspired by the brain cognitive mechanism to automatically learn graph patterns from emotional EEG and improve the performance of EEG emotion recognition. In the proposed BF-GCN, three graph branches, i.e., cognition-inspired functional graph branch, data-driven graph branch, and fused common graph branch, are first elaborately designed to automatically learn emotional cognitive graph patterns from emotional EEG signals. And then, the attention mechanism is adopted to further capture the brain activation graph patterns that are related to emotion cognition to achieve an efficient representation of emotional EEG signals. Essentially, the proposed BF-CGN model is a cognition-inspired graph learning neural network model, which utilizes the spectral graph filtering theory in the automatic learning and extracting of emotional EEG graph patterns. To evaluate the performance of the BF-GCN graph learning system, we conducted subject-dependent and subject-independent experiments on two public datasets, i.e., SEED and SEED-IV. The proposed BF-GCN graph learning system has achieved 97.44% (SEED) and 89.55% (SEED-IV) in subject-dependent experiments, and the results in subject-independent experiments have achieved 92.72% (SEED) and 82.03% (SEED-IV), respectively. The state-of-the-art performance indicates that the proposed BF-GCN graph learning system has a robust performance in EEG-based emotion recognition, which provides a promising direction for affective computing.

A hybrid critical channels and optimal feature subset selection framework for EEG fatigue recognition

Fatigue driving is one of the potential factors threatening road safety, and monitoring drivers' mental state through electroencephalography (EEG) can effectively prevent such risks. In this paper, a new model, DE-GFRJMCMC, is proposed for selecting critical channels and optimal feature subsets from EEG data to improve the accuracy of fatigue driving recognition. The model is validated on the SEED-VIG dataset. The model first selects critical EEG channels using the Differential Evolution (DE) algorithm, extracting important electrode channel information to enhance recognition accuracy. These electrode channels are used to construct a Functional Brain Network (FBN), from which the topological feature set is extracted. Empirical Mode Decomposition (EMD) is then applied to extract the intrinsic mode components as network nodes, thereby reducing the influence of the number of electrode channels on the brain functional network. The topological features extracted from these components form the suboptimal feature set. To minimize redundant information, we propose an improved Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm for selecting the optimal feature subset, ensuring both the efficiency and accuracy of fatigue recognition. The optimal feature subsets were input into various classifiers, and the results showed that the K-Nearest Neighbor (KNN)-based classifier achieved the highest recognition accuracy of 96.11% ± 0.43%, demonstrating the method's stability and robustness. Compared to similar studies, this model shows superior performance in fatigue driving recognition, which is of significant value for research on fatigue driving detection and prevention.

Crucial rhythms and subnetworks for emotion processing extracted by an interpretable deep learning framework from EEG networks

Electroencephalogram (EEG) brain networks describe the driving and synchronous relationships among multiple brain regions and can be used to identify different emotional states. However, methods for extracting interpretable structural features from brain networks are still lacking. In the current study, a novel deep learning structure comprising both an attention mechanism and a domain adversarial strategy is proposed to extract discriminant and interpretable features from brain networks. Specifically, the attention mechanism enhances the contribution of crucial rhythms and subnetworks for emotion recognition, whereas the domain-adversarial module improves the generalization performance of our proposed model for cross-subject tasks. We validated the effectiveness of the proposed method for subject-independent emotion recognition tasks with the SJTU Emotion EEG Dataset (SEED) and the EEGs recorded in our laboratory. The experimental results showed that the proposed method can effectively improve the classification accuracy of different emotions compared with commonly used methods such as domain adversarial neural networks. On the basis of the extracted network features, we also revealed crucial rhythms and subnetwork structures for emotion processing, which are consistent with those found in previous studies. Our proposed method not only improves the classification performance of brain networks but also provides a novel tool for revealing emotion processing mechanisms.

Hybrid Network Using Dynamic Graph Convolution and Temporal Self-Attention for EEG-Based Emotion Recognition

The electroencephalogram (EEG) signal has become a highly effective decoding target for emotion recognition and has garnered significant attention from researchers. Its spatial topological and time-dependent characteristics make it crucial to explore both spatial information and temporal information for accurate emotion recognition. However, existing studies often focus on either spatial or temporal aspects of EEG signals, neglecting the joint consideration of both perspectives. To this end, this article proposes a hybrid network consisting of a dynamic graph convolution (DGC) module and temporal self-attention representation (TSAR) module, which concurrently incorporates the representative knowledge of spatial topology and temporal context into the EEG emotion recognition task. Specifically, the DGC module is designed to capture the spatial functional relationships within the brain by dynamically updating the adjacency matrix during the model training process. Simultaneously, the TSAR module is introduced to emphasize more valuable time segments and extract global temporal features from EEG signals. To fully exploit the interactivity between spatial and temporal information, the hierarchical cross-attention fusion (H-CAF) module is incorporated to fuse the complementary information from spatial and temporal features. Extensive experimental results on the DEAP, SEED, and SEED-IV datasets demonstrate that the proposed method outperforms other state-of-the-art methods.

Emotion recognition using hierarchical spatial-temporal learning transformer from regional to global brain

Emotion recognition is an essential but challenging task in human-computer interaction systems due to the distinctive spatial structures and dynamic temporal dependencies associated with each emotion. However, current approaches fail to accurately capture the intricate effects of electroencephalogram (EEG) signals across different brain regions on emotion recognition. Therefore, this paper designs a transformer-based method, denoted by R2G-STLT, which relies on a spatial-temporal transformer encoder with regional to global hierarchical learning that learns the representative spatiotemporal features from the electrode level to the brain-region level. The regional spatial-temporal transformer (RST-Trans) encoder is designed to obtain spatial information and context dependence at the electrode level aiming to learn the regional spatiotemporal features. Then, the global spatial-temporal transformer (GST-Trans) encoder is utilized to extract reliable global spatiotemporal features, reflecting the impact of various brain regions on emotion recognition tasks. Moreover, the multi-head attention mechanism is placed into the GST-Trans encoder to empower it to capture the long-range spatial-temporal information among the brain regions. Finally, subject-independent experiments are conducted on each frequency band of the DEAP, SEED, and SEED-IV datasets to assess the performance of the proposed model. Results indicate that the R2G-STLT model surpasses several state-of-the-art approaches.

Learning Interpretable Brain Functional Connectivity via Self-Supervised Triplet Network With Depth-Wise Attention

Brain functional connectivity has been widely explored to reveal the functional interaction dynamics between the brain regions. However, conventional connectivity measures rely on deterministic models demanding application-specific empirical analysis, while deep learning approaches focus on finding discriminative features for state classification, having limited capability to capture the interpretable connectivity characteristics. To address the challenges, this study proposes a self-supervised triplet network with depth-wise attention (TripletNet-DA) to generate the functional connectivity: 1) TripletNet-DA firstly utilizes channel-wise transformations for temporal data augmentation, where the correlated & uncorrelated sample pairs are constructed for self-supervised training, 2) Channel encoder is designed with a convolution network to extract the deep features, while similarity estimator is employed to generate the similarity pairs and the functional connectivity representations, 3) TripletNet-DA applies Triplet loss with anchor-negative similarity penalty for model training, where the similarities of uncorrelated sample pairs are minimized to enhance model's learning capability. Experimental results on pathological EEG datasets (Autism Spectrum Disorder, Major Depressive Disorder) indicate that 1) TripletNet-DA demonstrates superiority in both ASD discrimination and MDD classification than the state-of-the-art counterparts, where the connectivity features in beta & gamma bands have respectively achieved the accuracy of 97.05%, 98.32% for ASD discrimination, 89.88%, 91.80% for MDD classification in the eyes-closed condition and 90.90%, 92.26% in the eyes-open condition, 2) TripletNet-DA enables to uncover significant differences of functional connectivity between ASD EEG and TD ones, and the prominent connectivity links are in accordance with the empirical findings, thus providing potential biomarkers for clinical ASD analysis.

Global and multi-partition local network analysis of scalp EEG in West syndrome before and after treatment

West syndrome is an epileptic disease that seriously affects the normal growth and development of infants in early childhood. Based on the methods of brain topological network and graph theory, this article focuses on three clinical states of patients before and after treatment. In addition to discussing bidirectional and unidirectional global networks from the perspective of computational principles, a more in-depth analysis of local intra-network and inter-network characteristics of multi-partitioned networks is also performed. The spatial feature distribution based on feature path length is introduced for the first time. The results show that the bidirectional network has better significant differentiation. The rhythmic feature change trend and spatial characteristic distribution of this network can be used as a measure of the impact on global information processing in the brain after treatment. And localized brain regions variability in features and differences in the ability to interact with information between brain regions have potential as biomarkers for medication assessment in WEST syndrome. The above shows specific conclusions on the interaction relationship and consistency of macro-network and micro-network, which may have a positive effect on patients' treatment and prognosis management.

Attention-Based Temporal Graph Representation Learning for EEG-Based Emotion Recognition

Due to the objectivity of emotional expression in the central nervous system, EEG-based emotion recognition can effectively reflect humans' internal emotional states. In recent years, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have made significant strides in extracting local features and temporal dependencies from EEG signals. However, CNNs ignore spatial distribution information from EEG electrodes; moreover, RNNs may encounter issues such as exploding/vanishing gradients and high time consumption. To address these limitations, we propose an attention-based temporal graph representation network (ATGRNet) for EEG-based emotion recognition. Firstly, a hierarchical attention mechanism is introduced to integrate feature representations from both frequency bands and channels ordered by priority in EEG signals. Second, a graph convolutional neural network with top-k operation is utilized to capture internal relationships between EEG electrodes under different emotion patterns. Next, a residual-based graph readout mechanism is applied to accumulate the EEG feature node-level representations into graph-level representations. Finally, the obtained graph-level representations are fed into a temporal convolutional network (TCN) to extract the temporal dependencies between EEG frames. We evaluated our proposed ATGRNet on the SEED, DEAP and FACED datasets. The experimental findings show that the proposed ATGRNet surpasses the state-of-the-art graph-based mehtods for EEG-based emotion recognition.

PSPN: Pseudo-Siamese Pyramid Network for multimodal emotion analysis

Emotion recognition plays an important role in human life and healthcare. The EEG has been extensively researched as an objective indicator of intense emotions. However, current existing methods lack sufficient analysis of shallow and deep EEG features. In addition, human emotions are complex and variable, making it difficult to comprehensively represent emotions using a single-modal signal. As a signal associated with gaze tracking and eye movement detection, Eye-related signals provide various forms of supplementary information for multimodal emotion analysis. Therefore, we propose a Pseudo-Siamese Pyramid Network (PSPN) for multimodal emotion analysis. The PSPN model employs a Depthwise Separable Convolutional Pyramid (DSCP) to extract and integrate intrinsic emotional features at various levels and scales from EEG signals. Simultaneously, we utilize a fully connected subnetwork to extract the external emotional features from eye-related signals. Finally, we introduce a Pseudo-Siamese network that integrates a flexible cross-modal dual-branch subnetwork to collaboratively utilize EEG emotional features and eye-related behavioral features, achieving consistency and complementarity in multimodal emotion recognition. For evaluation, we conducted experiments on the DEAP and SEED-IV public datasets. The experimental results demonstrate that multimodal fusion significantly improves the accuracy of emotion recognition compared to single-modal approaches. Our PSPN model achieved the best accuracy of 96.02% and 96.45% on the valence and arousal dimensions of the DEAP dataset, and 77.81% on the SEED-IV dataset, respectively. Our code link is: https://github.com/Yinyanyan003/PSPN.git.

Enhancing local representation learning through global-local integration with functional connectivity for EEG-based emotion recognition

Emotion recognition based on electroencephalogram (EEG) signals is crucial in understanding human affective states. Current research has limitations in extracting local features. The representation capabilities of local features are limited, making it difficult to comprehensively capture emotional information. In this study, a novel approach is proposed to enhance local representation learning through global-local integration with functional connectivity for EEG-based emotion recognition. By leveraging the functional connectivity of brain regions, EEG signals are divided into global embeddings that represent comprehensive brain connectivity patterns throughout the entire process and local embeddings that reflect dynamic interactions within specific brain functional networks at particular moments. Firstly, a convolutional feature extraction branch based on the residual network is designed to extract local features from the global embedding. To further improve the representation ability and accuracy of local features, a multidimensional collaborative attention (MCA) module is introduced. Secondly, the local features and patch embedded local embeddings are integrated into the feature coupling module (FCM), which utilizes hierarchical connections and enhanced cross-attention to couple region-level features, thereby enhancing local representation learning. Experimental results on three public datasets show that compared with other methods, this method improves accuracy by 4.92% on the DEAP, by 1.11% on the SEED, and by 7.76% on the SEED-IV, demonstrating its superior performance in emotion recognition tasks.

Effective brain connectivity related to non-painful thermal stimuli using EEG

Understanding the brain response to thermal stimuli is crucial in the sensory experience. This study focuses on non-painful thermal stimuli, which are sensations induced by temperature changes without causing discomfort. These stimuli are transmitted to the central nervous system through specific nerve fibers and are processed in various regions of the brain, including the insular cortex, the prefrontal cortex, and anterior cingulate cortex. Despite the prevalence of studies on painful stimuli, non-painful thermal stimuli have been less explored. This research aims to bridge this gap by investigating brain functional connectivity during the perception of non-painful warm and cold stimuli using electroencephalography (EEG) and the partial directed coherence technique (PDC). Our results demonstrate a clear contrast in the direction of information flow between warm and cold stimuli, particularly in the theta and alpha frequency bands, mainly in frontal and temporal regions. The use of PDC highlights the complexity of brain connectivity during these stimuli and reinforces the existence of different pathways in the brain to process different types of non-painful warm and cold stimuli.

The effects of spatial leakage correction on the reliability of EEG-based functional connectivity networks

Electroencephalography (EEG) functional connectivity (FC) estimates are confounded by the volume conduction problem. This effect can be greatly reduced by applying FC measures insensitive to instantaneous, zero-lag dependencies (corrected measures). However, numerous studies showed that FC measures sensitive to volume conduction (uncorrected measures) exhibit higher reliability and higher subject-level identifiability. We tested how source reconstruction contributed to the reliability difference of EEG FC measures on a large (n = 201) resting-state data set testing eight FC measures (including corrected and uncorrected measures). We showed that the high reliability of uncorrected FC measures in resting state partly stems from source reconstruction: idiosyncratic noise patterns define a baseline resting-state functional network that explains a significant portion of the reliability of uncorrected FC measures. This effect remained valid for template head model-based, as well as individual head model-based source reconstruction. Based on our findings we made suggestions how to best use spatial leakage corrected and uncorrected FC measures depending on the main goals of the study.

Semi-supervised multi-source transfer learning for cross-subject EEG motor imagery classification

Electroencephalogram (EEG) motor imagery (MI) classification refers to the use of EEG signals to identify and classify subjects' motor imagery activities; this task has received increasing attention with the development of brain-computer interfaces (BCIs). However, the collection of EEG data is usually time-consuming and labor-intensive, which makes it difficult to obtain sufficient labeled data from the new subject to train a new model. Moreover, the EEG signals of different individuals exhibit significant differences, leading to a significant drop in the performance of a model trained on the existing subjects when directly classifying EEG signals acquired from new subjects. Therefore, it is crucial to make full use of the EEG data of the existing subjects and the unlabeled EEG data of the new target subject to improve the MI classification performance achieved for the target subject. This research study proposes a semi-supervised multi-source transfer (SSMT) learning model to address the above problems; the model learns informative and domain-invariant representations to address cross-subject MI-EEG classification tasks. In particular, a dynamic transferred weighting schema is presented to obtain the final predictions by integrating the weighted features derived from multi-source domains. The average accuracies achieved on two publicly available EEG datasets reach 83.57 % and 85.09 % , respectively, validating the effectiveness of the SSMT process. The SSMT process reveals the importance of informative and domain-invariant representations in MI classification tasks, as they make full use of the domain-invariant information acquired from each subject.

Constructing Bodily Emotion Maps Based on High-Density Body Surface Potentials for Psychophysiological Computing

Emotion is a complex physiological and psychological activity, accompanied by subjective physiological sensations and objective physiological changes. The body sensation map describes the changes in body sensation associated with emotion in a topographic manner, but it relies on subjective evaluations from participants. Physiological signals are a more reliable measure of emotion, but most research focuses on the central nervous system, neglecting the importance of the peripheral nervous system. In this study, a body surface potential mapping (BSPM) system was constructed, and an experiment was designed to induce emotions and obtain high-density body surface potential information under negative and non-negative emotions. Then, by constructing and analyzing the functional connectivity network of BSPs, the high-density electrophysiological characteristics are obtained and visualized as bodily emotion maps. The results showed that the functional connectivity network of BSPs under negative emotions had denser connections, and emotion maps based on local clustering coefficient (LCC) are consistent with BSMs under negative emotions. in addition, our features can classify negative and non-negative emotions with the highest classification accuracy of 80.77%. In conclusion, this study constructs an emotion map based on high-density BSPs, which offers a novel approach to psychophysiological computing.

E-MFNN: an emotion-multimodal fusion neural network framework for emotion recognition

Emotional recognition is a pivotal research domain in computer and cognitive science. Recent advancements have led to various emotion recognition methods, leveraging data from diverse sources like speech, facial expressions, electroencephalogram (EEG), electrocardiogram, and eye tracking (ET). This article introduces a novel emotion recognition framework, primarily targeting the analysis of users' psychological reactions and stimuli. It is important to note that the stimuli eliciting emotional responses are as critical as the responses themselves. Hence, our approach synergizes stimulus data with physical and physiological signals, pioneering a multimodal method for emotional cognition. Our proposed framework unites stimulus source data with physiological signals, aiming to enhance the accuracy and robustness of emotion recognition through data integration. We initiated an emotional cognition experiment to gather EEG and ET data alongside recording emotional responses. Building on this, we developed the Emotion-Multimodal Fusion Neural Network (E-MFNN), optimized for multimodal data fusion to process both stimulus and physiological data. We conducted extensive comparisons between our framework's outcomes and those from existing models, also assessing various algorithmic approaches within our framework. This comparison underscores our framework's efficacy in multimodal emotion recognition. The source code is publicly available at https://figshare.com/s/8833d837871c78542b29.

Cross-modal credibility modelling for EEG-based multimodal emotion recognition

Objective.The study of emotion recognition through electroencephalography (EEG) has garnered significant attention recently. Integrating EEG with other peripheral physiological signals may greatly enhance performance in emotion recognition. Nonetheless, existing approaches still suffer from two predominant challenges: modality heterogeneity, stemming from the diverse mechanisms across modalities, and fusion credibility, which arises when one or multiple modalities fail to provide highly credible signals.Approach.In this paper, we introduce a novel multimodal physiological signal fusion model that incorporates both intra-inter modality reconstruction and sequential pattern consistency, thereby ensuring a computable and credible EEG-based multimodal emotion recognition. For the modality heterogeneity issue, we first implement a local self-attention transformer to obtain intra-modal features for each respective modality. Subsequently, we devise a pairwise cross-attention transformer to reveal the inter-modal correlations among different modalities, thereby rendering different modalities compatible and diminishing the heterogeneity concern. For the fusion credibility issue, we introduce the concept of sequential pattern consistency to measure whether different modalities evolve in a consistent way. Specifically, we propose to measure the varying trends of different modalities, and compute the inter-modality consistency scores to ascertain fusion credibility.Main results.We conduct extensive experiments on two benchmarked datasets (DEAP and MAHNOB-HCI) with the subject-dependent paradigm. For the DEAP dataset, our method improves the accuracy by 4.58%, and the F1 score by 0.63%, compared to the state-of-the-art baseline. Similarly, for the MAHNOB-HCI dataset, our method improves the accuracy by 3.97%, and the F1 score by 4.21%. In addition, we gain much insight into the proposed framework through significance test, ablation experiments, confusion matrices and hyperparameter analysis. Consequently, we demonstrate the effectiveness of the proposed credibility modelling through statistical analysis and carefully designed experiments.Significance.All experimental results demonstrate the effectiveness of our proposed architecture and indicate that credibility modelling is essential for multimodal emotion recognition.

Decoding emotion with phase-amplitude fusion features of EEG functional connectivity network

Decoding emotional neural representations from the electroencephalographic (EEG)-based functional connectivity network (FCN) is of great scientific importance for uncovering emotional cognition mechanisms and developing harmonious human-computer interactions. However, existing methods mainly rely on phase-based FCN measures (e.g., phase locking value [PLV]) to capture dynamic interactions between brain oscillations in emotional states, which fail to reflect the energy fluctuation of cortical oscillations over time. In this study, we initially examined the efficacy of amplitude-based functional networks (e.g., amplitude envelope correlation [AEC]) in representing emotional states. Subsequently, we proposed an efficient phase-amplitude fusion framework (PAF) to fuse PLV and AEC and used common spatial pattern (CSP) to extract fused spatial topological features from PAF for multi-class emotion recognition. We conducted extensive experiments on the DEAP and MAHNOB-HCI datasets. The results showed that: (1) AEC-derived discriminative spatial network topological features possess the ability to characterize emotional states, and the differential network patterns of AEC reflect dynamic interactions in brain regions associated with emotional cognition. (2) The proposed fusion features outperformed other state-of-the-art methods in terms of classification accuracy for both datasets. Moreover, the spatial filter learned from PAF is separable and interpretable, enabling a description of affective activation patterns from both phase and amplitude perspectives.

A novel transformer autoencoder for multi-modal emotion recognition with incomplete data

Multi-modal signals have become essential data for emotion recognition since they can represent emotions more comprehensively. However, in real-world environments, it is often impossible to acquire complete data on multi-modal signals, and the problem of missing modalities causes severe performance degradation in emotion recognition. Therefore, this paper represents the first attempt to use a transformer-based architecture, aiming to fill the modality-incomplete data from partially observed data for multi-modal emotion recognition (MER). Concretely, this paper proposes a novel unified model called transformer autoencoder (TAE), comprising a modality-specific hybrid transformer encoder, an inter-modality transformer encoder, and a convolutional decoder. The modality-specific hybrid transformer encoder bridges a convolutional encoder and a transformer encoder, allowing the encoder to learn local and global context information within each particular modality. The inter-modality transformer encoder builds and aligns global cross-modal correlations and models long-range contextual information with different modalities. The convolutional decoder decodes the encoding features to produce more precise recognition. Besides, a regularization term is introduced into the convolutional decoder to force the decoder to fully leverage the complete and incomplete data for emotional recognition of missing data. 96.33%, 95.64%, and 92.69% accuracies are attained on the available data of the DEAP and SEED-IV datasets, and 93.25%, 92.23%, and 81.76% accuracies are obtained on the missing data. Particularly, the model acquires a 5.61% advantage with 70% missing data, demonstrating that the model outperforms some state-of-the-art approaches in incomplete multi-modal learning.

Differential Brain Activation for Four Emotions in VR-2D and VR-3D Modes

Similar to traditional imaging, virtual reality (VR) imagery encompasses nonstereoscopic (VR-2D) and stereoscopic (VR-3D) modes. Currently, Russell's emotional model has been extensively studied in traditional 2D and VR-3D modes, but there is limited comparative research between VR-2D and VR-3D modes. In this study, we investigate whether Russell's emotional model exhibits stronger brain activation states in VR-3D mode compared to VR-2D mode. By designing an experiment covering four emotional categories (high arousal-high pleasure (HAHV), high arousal-low pleasure (HALV), low arousal-low pleasure (LALV), and low arousal-high pleasure (LAHV)), EEG signals were collected from 30 healthy undergraduate and graduate students while watching videos in both VR modes. Initially, power spectral density (PSD) computations revealed distinct brain activation patterns in different emotional states across the two modes, with VR-3D videos inducing significantly higher brainwave energy, primarily in the frontal, temporal, and occipital regions. Subsequently, Differential entropy (DE) feature sets, selected via a dual ten-fold cross-validation Support Vector Machine (SVM) classifier, demonstrate satisfactory classification accuracy, particularly superior in the VR-3D mode. The paper subsequently presents a deep learning-based EEG emotion recognition framework, adeptly utilizing the frequency, spatial, and temporal information of EEG data to improve recognition accuracy. The contribution of each individual feature to the prediction probabilities is discussed through machine-learning interpretability based on Shapley values. The study reveals notable differences in brain activation states for identical emotions between the two modes, with VR-3D mode showing more pronounced activation.

Efficient Estimation of Directed Connectivity in Nonlinear and Nonstationary Spiking Neuron Networks

OBJECTIVE

Studying directed connectivity within spiking neuron networks can help understand neural mechanisms. Existing methods assume linear time-invariant neural dynamics with a fixed time lag in information transmission, while spiking networks usually involve complex dynamics that are nonlinear and nonstationary, and have varying time lags.

METHODS

We develop a Gated Recurrent Unit (GRU)-Point Process (PP) method to estimate directed connectivity within spiking networks. We use a GRU to describe the dependency of the target neuron's current firing rate on the source neurons' past spiking events and a PP to relate the target neuron's firing rate to its current 0-1 spiking event. The GRU model uses recurrent states and gate/activation functions to deal with varying time lags, nonlinearity, and nonstationarity in a parameter-efficient manner. We estimate the model using maximum likelihood and compute directed information as our measure of directed connectivity.

RESULTS

We conduct simulations using artificial spiking networks and a biophysical model of Parkinson's disease to show that GRU-PP systematically addresses varying time lags, nonlinearity, and nonstationarity, and estimates directed connectivity with high accuracy and data efficiency. We also use a non-human-primate dataset to show that GRU-PP correctly identifies the biophysically-plausible stronger PMd-to-M1 connectivity than M1-to-PMd connectivity during reaching. In all experiments, the GRU-PP consistently outperforms state-of-the-art methods.

CONCLUSION

The GRU-PP method efficiently estimates directed connectivity in varying time lag, nonlinear, and nonstationary spiking neuron networks.

SIGNIFICANCE

The proposed method can serve as a directed connectivity analysis tool for investigating complex spiking neuron network dynamics.

EEG based emotion recognition by hierarchical bayesian spectral regression framework

Spectral regression (SR), a graph-based learning regression model, can be used to extract features from graphs to realize efficient dimensionality reduction. However, due to the SR method remains a regularized least squares problem and being defined in L2-norm space, the effect of artifacts in EEG signals cannot be efficiently resisted. In this work, to further improve the robustness of the graph-based regression models, we propose to utilize the prior distribution estimation in the Bayesian framework and develop a robust hierarchical Bayesian spectral regression framework (named HB-SR), which is designed with the hierarchical Bayesian ensemble strategies. In the proposed HB-SR, the impact of noises can be effectively reduced by the adaptive adjustment approach in model parameters with the data-driven manner. Specifically, in the current work, three different distributions have been elaborately designed to enhance the universality of the proposed HB-SR, i.e., Gaussian distribution, Laplace distribution, and Student-t distribution. To objectively evaluate the performance of the HB-SR framework, we conducted both simulation studies and emotion recognition experiments based on emotional EEG signals. Experimental results have consistently indicated that compared with other existing spectral regression methods, the proposed HB-SR can effectively suppress the influence of noises and achieve robust EEG emotion recognition.

ST-SCGNN: A Spatio-Temporal Self-Constructing Graph Neural Network for Cross-Subject EEG-Based Emotion Recognition and Consciousness Detection

In this paper, a novel spatio-temporal self-constructing graph neural network (ST-SCGNN) is proposed for cross-subject emotion recognition and consciousness detection. For spatio-temporal feature generation, activation and connection pattern features are first extracted and then combined to leverage their complementary emotion-related information. Next, a self-constructing graph neural network with a spatio-temporal model is presented. Specifically, the graph structure of the neural network is dynamically updated by the self-constructing module of the input signal. Experiments based on the SEED and SEED-IV datasets showed that the model achieved average accuracies of 85.90% and 76.37%, respectively. Both values exceed the state-of-the-art metrics with the same protocol. In clinical besides, patients with disorders of consciousness (DOC) suffer severe brain injuries, and sufficient training data for EEG-based emotion recognition cannot be collected. Our proposed ST-SCGNN method for cross-subject emotion recognition was first attempted in training in ten healthy subjects and testing in eight patients with DOC. We found that two patients obtained accuracies significantly higher than chance level and showed similar neural patterns with healthy subjects. Covert consciousness and emotion-related abilities were thus demonstrated in these two patients. Our proposed ST-SCGNN for cross-subject emotion recognition could be a promising tool for consciousness detection in DOC patients.

Decoding auditory-evoked response in affective states using wearable around-ear EEG system

Objective.In this paper, an around-ear EEG system is investigated as an alternative methodology to conventional scalp-EEG-based systems in classifying human affective states in the arousal-valence domain evoked in response to auditory stimuli.Approach.EEG recorded from around the ears is compared to EEG collected according to the international 10-20 system in terms of efficacy in an affective state classification task. A wearable device with eight dry EEG channels is designed for ear-EEG acquisition in this study. Twenty-one subjects participated in an experiment consisting of six sessions over three days using both ear and scalp-EEG acquisition methods. Experimental tasks consisted of listening to an auditory stimulus and self-reporting the elicited emotion in response to the said stimulus. Various features were used in tandem with asymmetry methods to evaluate binary classification performances of arousal and valence states using ear-EEG signals in comparison to scalp-EEG.Main results.We achieve an average accuracy of 67.09% ± 6.14 for arousal and 66.61% ± 6.14 for valence after training a multi-layer extreme learning machine with ear-EEG signals in a subject-dependent context in comparison to scalp-EEG approach which achieves an average accuracy of 68.59% ± 6.26 for arousal and 67.10% ± 4.99 for valence. In a subject-independent context, the ear-EEG approach achieves 63.74% ± 3.84 for arousal and 64.32% ± 6.38 for valence while the scalp-EEG approach achieves 64.67% ± 6.91 for arousal and 64.86% ± 5.95 for valence. The best results show no significant differences between ear-EEG and scalp-EEG signals for classifications of affective states.Significance.To the best of our knowledge, this paper is the first work to explore the use of around-ear EEG signals in emotion monitoring. Our results demonstrate the potential use of around-ear EEG systems for the development of emotional monitoring setups that are more suitable for use in daily affective life log systems compared to conventional scalp-EEG setups.

Recognizing emotions induced by wearable haptic vibration using noninvasive electroencephalogram

The integration of haptic technology into affective computing has led to a new field known as affective haptics. Nonetheless, the mechanism underlying the interaction between haptics and emotions remains unclear. In this paper, we proposed a novel haptic pattern with adaptive vibration intensity and rhythm according to the volume, and applied it into the emotional experiment paradigm. To verify its superiority, the proposed haptic pattern was compared with an existing haptic pattern by combining them with conventional visual-auditory stimuli to induce emotions (joy, sadness, fear, and neutral), and the subjects' EEG signals were collected simultaneously. The features of power spectral density (PSD), differential entropy (DE), differential asymmetry (DASM), and differential caudality (DCAU) were extracted, and the support vector machine (SVM) was utilized to recognize four target emotions. The results demonstrated that haptic stimuli enhanced the activity of the lateral temporal and prefrontal areas of the emotion-related brain regions. Moreover, the classification accuracy of the existing constant haptic pattern and the proposed adaptive haptic pattern increased by 7.71 and 8.60%, respectively. These findings indicate that flexible and varied haptic patterns can enhance immersion and fully stimulate target emotions, which are of great importance for wearable haptic interfaces and emotion communication through haptics.

Dynamic segregation and integration of brain functional networks associated with emotional arousal

The organization of brain functional networks dynamically changes with emotional stimuli, but its relationship to emotional behaviors is still unclear. In the DEAP dataset, we used the nested-spectral partition approach to identify the hierarchical segregation and integration of functional networks and investigated the dynamic transitions between connectivity states under different arousal conditions. The frontal and right posterior parietal regions were dominant for network integration whereas the bilateral temporal, left posterior parietal, and occipital regions were responsible for segregation and functional flexibility. High emotional arousal behavior was associated with stronger network integration and more stable state transitions. Crucially, the connectivity states of frontal, central, and right parietal regions were closely related to arousal ratings in individuals. Besides, we predicted the individual emotional performance based on functional connectivity activities. Our results demonstrate that brain connectivity states are closely associated with emotional behaviors and could be reliable and robust indicators for emotional arousal.

Less Is More: Brain Functional Connectivity Empowered Generalizable Intention Classification With Task-Relevant Channel Selection

Electroencephalography (EEG) signals are gaining popularity in Brain-Computer Interface (BCI)-based rehabilitation and neural engineering applications thanks to their portability and availability. Inevitably, the sensory electrodes on the entire scalp would collect signals irrelevant to the particular BCI task, increasing the risks of overfitting in machine learning-based predictions. While this issue is being addressed by scaling up the EEG datasets and handcrafting the complex predictive models, this also leads to increased computation costs. Moreover, the model trained for one set of subjects cannot easily be adapted to other sets due to inter-subject variability, which creates even higher over-fitting risks. Meanwhile, despite previous studies using either convolutional neural networks (CNNs) or graph neural networks (GNNs) to determine spatial correlations between brain regions, they fail to capture brain functional connectivity beyond physical proximity. To this end, we propose 1) removing task-irrelevant noises instead of merely complicating models; 2) extracting subject-invariant discriminative EEG encodings, by taking functional connectivity into account. Specifically, we construct a task-adaptive graph representation of the brain network based on topological functional connectivity rather than distance-based connections. Further, non-contributory EEG channels are excluded by selecting only functional regions relevant to the corresponding intention. We empirically show that the proposed approach outperforms the state-of-the-art, with around 1% and 11% improvements over CNN-based and GNN-based models, on performing motor imagery predictions. Also, the task-adaptive channel selection demonstrates similar predictive performance with only 20% of raw EEG data, suggesting a possible shift in direction for future works other than simply scaling up the model.

A Bimodal Emotion Recognition Approach through the Fusion of Electroencephalography and Facial Sequences

In recent years, human-computer interaction (HCI) systems have become increasingly popular. Some of these systems demand particular approaches for discriminating actual emotions through the use of better multimodal methods. In this work, a deep canonical correlation analysis (DCCA) based multimodal emotion recognition method is presented through the fusion of electroencephalography (EEG) and facial video clips. A two-stage framework is implemented, where the first stage extracts relevant features for emotion recognition using a single modality, while the second stage merges the highly correlated features from the two modalities and performs classification. Convolutional neural network (CNN) based Resnet50 and 1D-CNN (1-Dimensional CNN) have been utilized to extract features from facial video clips and EEG modalities, respectively. A DCCA-based approach was used to fuse highly correlated features, and three basic human emotion categories (happy, neutral, and sad) were classified using the SoftMax classifier. The proposed approach was investigated based on the publicly available datasets called MAHNOB-HCI and DEAP. Experimental results revealed an average accuracy of 93.86% and 91.54% on the MAHNOB-HCI and DEAP datasets, respectively. The competitiveness of the proposed framework and the justification for exclusivity in achieving this accuracy were evaluated by comparison with existing work.

Multi-Domain Encoding of Spatiotemporal Dynamics in EEG for Emotion Recognition

The common goal of the studies is to map any emotional states encoded from electroencephalogram (EEG) into 2-dimensional arousal-valance scores. It is still challenging due to each emotion having its specific spatial structure and dynamic dependence over the distinct time segments among EEG signals. This paper aims to model human dynamic emotional behavior by considering the location connectivity and context dependency of brain electrodes. Thus, we designed a hybrid EEG modeling method that mainly adopts the attention mechanism, combining a multi-domain spatial transformer (MST) module and a dynamic temporal transformer (DTT) module, named MSDTTs. Specifically, the MST module extracts single-domain and cross-domain features from different brain regions and fuses them into multi-domain spatial features. Meanwhile, the temporal dynamic excitation (TDE) is inserted into the multi-head convolutional transformer to form the DTT module. These two blocks work together to activate and extract the emotion-related dynamic temporal features within the DTT module. Furthermore, we place the convolutional mapping into the transformer structure to mine the static context features among the keyframes. Overall results show that high classification accuracy of 98.91%/0.14% was obtained by the $\beta$ frequency band of the DEAP dataset, and 97.52%/0.12% and 96.70%/0.26% were obtained by the $\gamma$ frequency band of SEED and SEED-IV datasets. Empirical experiments indicate that our proposed method can achieve remarkable results in comparison with state-of-the-art algorithms.

Emotion recognition using spatial-temporal EEG features through convolutional graph attention network

Objective.Constructing an efficient human emotion recognition model based on electroencephalogram (EEG) signals is significant for realizing emotional brain-computer interaction and improving machine intelligence.Approach.In this paper, we present a spatial-temporal feature fused convolutional graph attention network (STFCGAT) model based on multi-channel EEG signals for human emotion recognition. First, we combined the single-channel differential entropy (DE) feature with the cross-channel functional connectivity (FC) feature to extract both the temporal variation and spatial topological information of EEG. After that, a novel convolutional graph attention network was used to fuse the DE and FC features and further extract higher-level graph structural information with sufficient expressive power for emotion recognition. Furthermore, we introduced a multi-headed attention mechanism in graph neural networks to improve the generalization ability of the model.Main results.We evaluated the emotion recognition performance of our proposed model on the public SEED and DEAP datasets, which achieved a classification accuracy of 99.11% ± 0.83% and 94.83% ± 3.41% in the subject-dependent and subject-independent experiments on the SEED dataset, and achieved an accuracy of 91.19% ± 1.24% and 92.03% ± 4.57% for discrimination of arousal and valence in subject-independent experiments on DEAP dataset. Notably, our model achieved state-of-the-art performance on cross-subject emotion recognition tasks for both datasets. In addition, we gained insight into the proposed frame through both the ablation experiments and the analysis of spatial patterns of FC and DE features.Significance.All these results prove the effectiveness of the STFCGAT architecture for emotion recognition and also indicate that there are significant differences in the spatial-temporal characteristics of the brain under different emotional states.

EEG Conformer: Convolutional Transformer for EEG Decoding and Visualization

Due to the limited perceptual field, convolutional neural networks (CNN) only extract local temporal features and may fail to capture long-term dependencies for EEG decoding. In this paper, we propose a compact Convolutional Transformer, named EEG Conformer, to encapsulate local and global features in a unified EEG classification framework. Specifically, the convolution module learns the low-level local features throughout the one-dimensional temporal and spatial convolution layers. The self-attention module is straightforwardly connected to extract the global correlation within the local temporal features. Subsequently, the simple classifier module based on fully-connected layers is followed to predict the categories for EEG signals. To enhance interpretability, we also devise a visualization strategy to project the class activation mapping onto the brain topography. Finally, we have conducted extensive experiments to evaluate our method on three public datasets in EEG-based motor imagery and emotion recognition paradigms. The experimental results show that our method achieves state-of-the-art performance and has great potential to be a new baseline for general EEG decoding. The code has been released in https://github.com/eeyhsong/EEG-Conformer.

Decoding Articulation Motor Imagery Using Early Connectivity Information in the Motor Cortex: A Functional Near-Infrared Spectroscopy Study

Brain computer interface (BCI) based on speech imagery can help people with motor disorders communicate their thoughts to the outside world in a natural way. Due to being portable, non-invasive, and safe, functional near-infrared spectroscopy (fNIRS) is preferred for developing BCIs. Previous BCIs based on fNIRS mainly relied on activation information, which ignored the functional connectivity between neural areas. In this study, a 4-class speech imagery BCI based on fNIRS is presented to decode simplified articulation motor imagery (only the movements of jaw and lip were retained) of different vowels. Synchronization information in the motor cortex was extracted as features. In multiclass (four classes) settings, the mean subject-dependent classification accuracies approximated or exceeded 40% in the 0-2.5 s and 0-10 s time windows, respectively. In binary class settings (the average classification accuracies of all pairwise comparisons between two vowels), the mean subject-dependent classification accuracies exceeded 70% in the 0-2.5 s and 0-10 s time windows. These results demonstrate that connectivity features can effectively differentiate different vowels even if the time window size was reduced from 10 s to 2.5 s and the decoding performance in both the time windows was almost the same. This finding suggests that speech imagery BCI based on fNIRS can be further optimized in terms of feature extraction and command generation time reduction. In addition, simplified articulation motor imagery of vowels can be distinguished, and therefore, the potential contribution of articulation motor imagery information extracted from the motor cortex should be emphasized in speech imagery BCI based on fNIRS to improve decoding performance.

Emotion recognition with residual network driven by spatial-frequency characteristics of EEG recorded from hearing-impaired adults in response to video clips

In recent years, emotion recognition based on electroencephalography (EEG) signals has attracted plenty of attention. Most of the existing works focused on normal or depressed people. Due to the lack of hearing ability, it is difficult for hearing-impaired people to express their emotions through language in their social activities. In this work, we collected the EEG signals of hearing-impaired subjects when they were watching six kinds of emotional video clips (happiness, inspiration, neutral, anger, fear, and sadness) for emotion recognition. The biharmonic spline interpolation method was utilized to convert the traditional frequency domain features, Differential Entropy (DE), Power Spectral Density (PSD), and Wavelet Entropy (WE) into the spatial domain. The patch embedding (PE) method was used to segment the feature map into the same patch to obtain the differences in the distribution of emotional information among brain regions. For feature classification, a compact residual network with Depthwise convolution (DC) and Pointwise convolution (PC) is proposed to separate spatial and channel mixing dimensions to better extract information between channels. Dependent subject experiments based on 70% training sets and 30% testing sets were performed. The results showed that the average classification accuracies by PE (DE), PE (PSD), and PE (WE) were 91.75%, 85.53%, and 75.68%, respectively which were improved by 11.77%, 23.54%, and 16.61% compared with DE, PSD, and WE. Moreover, the comparison experiments were carried out on the SEED and DEAP datasets with PE (DE), which achieved average accuracies of 90.04% (positive, neutral, and negative) and 88.75% (high valence and low valence). By exploring the emotional brain regions, we found that the frontal, parietal, and temporal lobes of hearing-impaired people were associated with emotional activity compared to normal people whose main emotional brain area was the frontal lobe.

Study on the diversity of mental states and neuroplasticity of the brain during human-machine interaction

Introduction

With the increasing demand for human-machine collaboration systems, more and more attention has been paid to the influence of human factors on the performance and security of the entire system. Especially in high-risk, high-precision, and difficult special tasks (such as space station maintenance tasks, anti-terrorist EOD tasks, surgical robot teleoperation tasks, etc.), there are higher requirements for the operator's perception and cognitive level. However, as the human brain is a complex and open giant system, the perception ability and cognitive level of the human are dynamically variable, so that it will seriously affect the performance and security of the whole system.

Methods

The method proposed in this paper innovatively explained this phenomenon from two dimensions of brain space and time and attributed the dynamic changes of perception, cognitive level, and operational skills to the mental state diversity and the brain neuroplasticity. In terms of the mental state diversity, the mental states evoked paradigm and the functional brain network analysis method during work were proposed. In terms of neuroplasticity, the cognitive training intervention paradigm and the functional brain network analysis method were proposed. Twenty-six subjects participated in the mental state evoked experiment and the cognitive training intervention experiment.

Results

The results showed that (1) the mental state of the subjects during work had the characteristics of dynamic change, and due to the influence of stimulus conditions and task patterns, the mental state showed diversity. There were significant differences between functional brain networks in different mental states, the information processing efficiency and the mechanism of brain area response had changed significantly. (2) The small-world attributes of the functional brain network of the subjects before and after the cognitive training experiment were significantly different. The brain had adjusted the distribution of information flow and resources, reducing costs and increasing efficiency as a whole. It was demonstrated that the global topology of the cortical connectivity network was reconfigured and neuroplasticity was altered through cognitive training intervention.

Discussion

In summary, this paper revealed that mental state and neuroplasticity could change the information processing efficiency and the response mechanism of brain area, thus causing the change of perception, cognitive level and operational skills, which provided a theoretical basis for studying the relationship between neural information processing and behavior.

Graph Theoretical Analysis of EEG Functional Connectivity Patterns and Fusion with Physiological Signals for Emotion Recognition

Emotion recognition is a key attribute for realizing advances in human-computer interaction, especially when using non-intrusive physiological sensors, such as electroencephalograph (EEG) and electrocardiograph. Although functional connectivity of EEG has been utilized for emotion recognition, the graph theory analysis of EEG connectivity patterns has not been adequately explored. The exploitation of brain network characteristics could provide valuable information regarding emotions, while the combination of EEG and peripheral physiological signals can reveal correlation patterns of human internal state. In this work, a graph theoretical analysis of EEG functional connectivity patterns along with fusion between EEG and peripheral physiological signals for emotion recognition has been proposed. After extracting functional connectivity from EEG signals, both global and local graph theory features are extracted. Those features are concatenated with statistical features from peripheral physiological signals and fed to different classifiers and a Convolutional Neural Network (CNN) for emotion recognition. The average accuracy on the DEAP dataset using CNN was 55.62% and 57.38% for subject-independent valence and arousal classification, respectively, and 83.94% and 83.87% for subject-dependent classification. Those scores went up to 75.44% and 78.77% for subject-independent classification and 88.27% and 90.84% for subject-dependent classification using a feature selection algorithm, exceeding the current state-of-the-art results.

Exploration of effective electroencephalography features for the recognition of different valence emotions

Recent studies have shown that the recognition and monitoring of different valence emotions can effectively avoid the occurrence of human errors due to the decline in cognitive ability. The quality of features directly affects emotion recognition results, so this manuscript explores the effective electroencephalography (EEG) features for the recognition of different valence emotions. First, 110 EEG features were extracted from the time domain, frequency domain, time-frequency domain, spatial domain, and brain network, including all the current mainly used features. Then, the classification performance, computing time, and important electrodes of each feature were systematically compared and analyzed on the self-built dataset involving 40 subjects and the public dataset DEAP. The experimental results show that the first-order difference, second-order difference, high-frequency power, and high-frequency differential entropy features perform better in the recognition of different valence emotions. Also, the time-domain features, especially the first-order difference features and second-order difference features, have less computing time, so they are suitable for real-time emotion recognition applications. Besides, the features extracted from the frontal, temporal, and occipital lobes are more effective than others for the recognition of different valence emotions. Especially, when the number of electrodes is reduced by 3/4, the classification accuracy of using features from 16 electrodes located in these brain regions is 91.8%, which is only about 2% lower than that of using all electrodes. The study results can provide an important reference for feature extraction and selection in emotion recognition based on EEG.

A Review of AI Cloud and Edge Sensors, Methods, and Applications for the Recognition of Emotional, Affective and Physiological States

Affective, emotional, and physiological states (AFFECT) detection and recognition by capturing human signals is a fast-growing area, which has been applied across numerous domains. The research aim is to review publications on how techniques that use brain and biometric sensors can be used for AFFECT recognition, consolidate the findings, provide a rationale for the current methods, compare the effectiveness of existing methods, and quantify how likely they are to address the issues/challenges in the field. In efforts to achieve the key goals of Society 5.0, Industry 5.0, and human-centered design better, the recognition of emotional, affective, and physiological states is progressively becoming an important matter and offers tremendous growth of knowledge and progress in these and other related fields. In this research, a review of AFFECT recognition brain and biometric sensors, methods, and applications was performed, based on Plutchik's wheel of emotions. Due to the immense variety of existing sensors and sensing systems, this study aimed to provide an analysis of the available sensors that can be used to define human AFFECT, and to classify them based on the type of sensing area and their efficiency in real implementations. Based on statistical and multiple criteria analysis across 169 nations, our outcomes introduce a connection between a nation's success, its number of Web of Science articles published, and its frequency of citation on AFFECT recognition. The principal conclusions present how this research contributes to the big picture in the field under analysis and explore forthcoming study trends.

EEG emotion recognition based on cross-frequency granger causality feature extraction and fusion in the left and right hemispheres

EEG emotion recognition based on Granger causality (GC) brain networks mainly focus on the EEG signal from the same-frequency bands, however, there are still some causality relationships between EEG signals in the cross-frequency bands. Considering the functional asymmetric of the left and right hemispheres to emotional response, this paper proposes an EEG emotion recognition scheme based on cross-frequency GC feature extraction and fusion in the left and right hemispheres. Firstly, we calculate the GC relationship of EEG signals according to the frequencies and hemispheres, and mainly focus on the causality of the cross-frequency EEG signals in left and right hemispheres. Then, to remove the redundant connections of the GC brain network, an adaptive two-stage decorrelation feature extraction scheme is proposed under the condition of maintaining the best emotion recognition performance. Finally, a multi-GC feature fusion scheme is designed to balance the recognition accuracy and feature number of each GC feature, which comprehensively considers the influence of the recognition accuracy and computational complexity. Experimental results on the DEAP emotion dataset show that the proposed scheme can achieve an average accuracy of 84.91% for four classifications, which improved the classification accuracy by up to 8.43% compared with that of the traditional same-frequency band GC features.

Recognition of Emotion by Brain Connectivity and Eye Movement

Simultaneous activation of brain regions (i.e., brain connection features) is an essential mechanism of brain activity in emotion recognition of visual content. The occipital cortex of the brain is involved in visual processing, but the frontal lobe processes cranial nerve signals to control higher emotions. However, recognition of emotion in visual content merits the analysis of eye movement features, because the pupils, iris, and other eye structures are connected to the nerves of the brain. We hypothesized that when viewing video content, the activation features of brain connections are significantly related to eye movement characteristics. We investigated the relationship between brain connectivity (strength and directionality) and eye movement features (left and right pupils, saccades, and fixations) when 47 participants viewed an emotion-eliciting video on a two-dimensional emotion model (valence and arousal). We found that the connectivity eigenvalues of the long-distance prefrontal lobe, temporal lobe, parietal lobe, and center are related to cognitive activity involving high valance. In addition, saccade movement was correlated with long-distance occipital-frontal connectivity. Finally, short-distance connectivity results showed emotional fluctuations caused by unconscious stimulation.

EEG Emotion Classification Network Based on Attention Fusion of Multi-Channel Band Features

Understanding learners' emotions can help optimize instruction sand further conduct effective learning interventions. Most existing studies on student emotion recognition are based on multiple manifestations of external behavior, which do not fully use physiological signals. In this context, on the one hand, a learning emotion EEG dataset (LE-EEG) is constructed, which captures physiological signals reflecting the emotions of boredom, neutrality, and engagement during learning; on the other hand, an EEG emotion classification network based on attention fusion (ECN-AF) is proposed. To be specific, on the basis of key frequency bands and channels selection, multi-channel band features are first extracted (using a multi-channel backbone network) and then fused (using attention units). In order to verify the performance, the proposed model is tested on an open-access dataset SEED (N = 15) and the self-collected dataset LE-EEG (N = 45), respectively. The experimental results using five-fold cross validation show the following: (i) on the SEED dataset, the highest accuracy of 96.45% is achieved by the proposed model, demonstrating a slight increase of 1.37% compared to the baseline models; and (ii) on the LE-EEG dataset, the highest accuracy of 95.87% is achieved, demonstrating a 21.49% increase compared to the baseline models.

The Multimodal Emotion Information Analysis of E-Commerce Online Pricing in Electronic Word of Mouth

E-commerce has developed rapidly, and product promotion refers to how e-commerce promotes consumers' consumption activities. The demand and computational complexity in the decision-making process are urgent problems to be solved to optimize dynamic pricing decisions of the e-commerce product lines. Therefore, a Q-learning algorithm model based on the neural network is proposed on the premise of multimodal emotion information recognition and analysis, and the dynamic pricing problem of the product line is studied. The results show that a multi-modal fusion model is established through the multi-modal fusion of speech emotion recognition and image emotion recognition to classify consumers' emotions. Then, they are used as auxiliary materials for understanding and analyzing the market demand. The long short-term memory (LSTM) classifier performs excellent image feature extraction. The accuracy rate is 3.92%-6.74% higher than that of other similar classifiers, and the accuracy rate of the image single-feature optimal model is 9.32% higher than that of the speech single-feature model.

Multimodal Sentiment Analysis of Online Product Information Based on Text Mining Under the Influence of Social Media

Currently, with the dramatic increase in social media users and the greater variety of online product information, manual processing of this information is time-consuming and labour-intensive. Therefore, based on the text mining of online information, this paper analyzes the text representation method of online information, discusses the long short-term memory network, and constructs an interactive attention graph convolutional network (IAGCN) model based on graph convolutional neural network (GCNN) and attention mechanism to study the multimodal sentiment analysis (MSA) of online product information. The results show that the IAGCN model improves the accuracy by 4.78% and the F1 value by 29.25% compared with the pure interactive attention network. Meanwhile, it is found that the performance of the model is optimal when the GCNN is two layers and uses syntactic position attention. This research has important practical significance for MSA of online product information in social media.