EEG-Based Emotion Recognition Using an Improved Weighted Horizontal Visibility Graph

EEG-based deception detection using weighted dual perspective visibility graph analysis

Deception detection is a critical aspect across various domains. Integrating advanced signal processing techniques, particularly in neuroscientific studies, has opened new avenues for exploring deception at a deeper level. This study uses electroencephalogram (EEG) signals from a balanced cohort of 22 participants, consisting of both males and females, aged between 22 and 29, engaged in a visual task for instructed deception. We propose a novel approach in the realm of deception detection utilizing the Weighted Dual Perspective Visibility Graph (WDPVG) method to decode instructed deception by converting average epochs from each EEG channel into a complex network. Six graph-based features, including average and deviation of strength, weighted clustering coefficient, weighted clustering coefficient entropy, average weighted shortest path length, and modularity, are extracted, comprehensively representing the underlying brain dynamics associated with deception. Subsequently, these features are employed for classification using three distinct algorithms: K Nearest Neighbors (KNN), Support Vector Machine (SVM), and Decision Tree (DT). Experimental results reveal promising accuracy rates for KNN (66.64%), SVM (86.25%), and DT (82.46%). Furthermore, the features distributions of EEG networks are analyzed across different brain lobes, comparing truth-telling and lying modes. These analyses reveal the frontal and parietal lobes' potential in distinguishing between truth and deception, highlighting their active role during deceptive behavior. The findings demonstrate the WDPVG method's effectiveness in decoding deception from EEG signals, offering insights into the neural basis of deceptive behavior. This research could enhance the understanding of neuroscience and deception detection, providing a framework for future real-world applications.

Dynamical graph neural network with attention mechanism for epilepsy detection using single channel EEG

Epilepsy is a chronic brain disease, and identifying seizures based on electroencephalogram (EEG) signals would be conducive to implement interventions to help patients reduce impairment and improve quality of life. In this paper, we propose a classification algorithm to apply dynamical graph neural network with attention mechanism to single channel EEG signals. Empirical mode decomposition (EMD) are adopted to construct graphs and the optimal adjacency matrix is obtained by model optimization. A multilayer dynamic graph neural network with attention mechanism is proposed to learn more discriminative graph features. The MLP-pooling structure is proposed to fuse graph features. We performed 12 classification tasks on the epileptic EEG database of the University of Bonn, and experimental results showed that using 25 runs of ten-fold cross-validation produced the best classification results with an average of 99.83[Formula: see text] accuracy, 99.91[Formula: see text] specificity, 99.78[Formula: see text] sensitivity, 99.87[Formula: see text] precision, and 99.47[Formula: see text] [Formula: see text] score for the 12 classification tasks.

Visibility graph for time series prediction and image classification: a review

The analysis of time series and images is significant across different fields due to their widespread applications. In the past few decades, many approaches have been developed, including data-driven artificial intelligence methods, mechanism-driven physical methods, and hybrid mechanism and data-driven models. Complex networks have been used to model numerous complex systems due to its characteristics, including time series prediction and image classification. In order to map time series and images into complex networks, many visibility graph algorithms have been developed, such as horizontal visibility graph, limited penetrable visibility graph, multiplex visibility graph, and image visibility graph. The family of visibility graph algorithms will construct different types of complex networks, including (un-) weighted, (un-) directed, and (single-) multi-layered networks, thereby focusing on different kinds of properties. Different types of visibility graph algorithms will be reviewed in this paper. Through exploring the topological structure and information in the network based on statistical physics, the property of time series and images can be discovered. In order to forecast (multivariate) time series, several variations of local random walk algorithms and different information fusion approaches are applied to measure the similarity between nodes in the network. Different forecasting frameworks are also proposed to consider the information in the time series based on the similarity. In order to classify the image, several machine learning models (such as support vector machine and linear discriminant) are used to classify images based on global features, local features, and multiplex features. Through various simulations on a variety of datasets, researchers have found that the visibility graph algorithm outperformed existing algorithms, both in time series prediction and image classification. Clearly, complex networks are closely connected with time series and images by visibility graph algorithms, rendering complex networks to be an important tool for understanding the characteristics of time series and images. Finally, we conclude in the last section with future outlooks for the visibility graph.

Classification of Contrasting Discrete Emotional States Indicated by EEG Based Graph Theoretical Network Measures

The present study shows new findings that reveal the high association between emotional arousal and neuro-functional brain connectivity measures. For this purpose, contrasting discrete emotional states (happiness vs sadness, amusement vs disgust, calmness vs excitement, calmness vs anger, fear vs anger) are classified by using Support Vector Machines (SVMs) driven by Graph Theoretical segregation (clustering coefficients, transitivity, modularity) and integration (global efficiency, local efficiency) measures of the brain network. Emotional EEG data mediated by short duration video film clips is downloaded from publicly available database called DREAMER. Pearson Correlation (PC) and Spearman Correlation have been examined to estimate statistical dependencies between relatively shorter (6 sec) and longer (12 sec) non-overlapped EEG segments across the cortex. Then the corresponding brain connectivity encoded as a graph is transformed into binary numbers with respect to two different thresholds (60%max and mean). Statistical differences between contrasting emotions are obtained by using both one-way Anova tests and step-wise logistic regression modelling in accordance with variables (dependency estimation, segment length, threshold, network measure). Combined integration measures provided the highest classification accuracies (CAs) (75.00% 80.65%) when PC is applied to longer segments in accordance with particular threshold as the mean. The segregation measures also provided useful CAs (74.13% 80.00%), while the combination of both measures did not. The results reveal that discrete emotional states are characterized by balanced network measures even if both segregation and integration measures vary depending on arousal scores of audio-visual stimuli due to neurotransmitter release during video watching.

Cross-subject emotion recognition using visibility graph and genetic algorithm-based convolution neural network

An efficient emotion recognition model is an important research branch in electroencephalogram (EEG)-based brain-computer interfaces. However, the input of the emotion recognition model is often a whole set of EEG channels obtained by electrodes placed on subjects. The unnecessary information produced by redundant channels affects the recognition rate and depletes computing resources, thereby hindering the practical applications of emotion recognition. In this work, we aim to optimize the input of EEG channels using a visibility graph (VG) and genetic algorithm-based convolutional neural network (GA-CNN). First, we design an experiment to evoke three types of emotion states using movies and collect the multi-channel EEG signals of each subject under different emotion states. Then, we construct VGs for each EEG channel and derive nonlinear features representing each EEG channel. We employ the genetic algorithm (GA) to find the optimal subset of EEG channels for emotion recognition and use the recognition results of the CNN as fitness values. The experimental results show that the recognition performance of the proposed method using a subset of EEG channels is superior to that of the CNN using all channels for each subject. Last, based on the subset of EEG channels searched by the GA-CNN, we perform cross-subject emotion recognition tasks employing leave-one-subject-out cross-validation. These results demonstrate the effectiveness of the proposed method in recognizing emotion states using fewer EEG channels and further enrich the methods of EEG classification using nonlinear features.

Emotion Recognition: An Evaluation of ERP Features Acquired from Frontal EEG Electrodes

The challenge to develop an affective Brain Computer Interface requires the understanding of emotions psychologically, physiologically as well as analytically. To make the analysis and classification of emotions possible, emotions have been represented in a two-dimensional or three-dimensional space represented by arousal and valence domains or arousal, valence and dominance domains, respectively. This paper presents the classification of emotions into four classes in an arousal–valence plane using the orthogonal nature of emotions. The average Event Related Potential (ERP) attributes and differential of average ERPs acquired from the frontal region of 24 subjects have been used to classify emotions into four classes. The attributes acquired from the frontal electrodes, viz., Fp1, Fp2, F3, F4, F8 and Fz, have been used for developing a classifier. The four-class subject-independent emotion classification results in the range of 67–83% have been obtained. Using three classifiers, a mid-range accuracy of 85% has been obtained, which is considerably better than existing studies on ERPs.