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

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 21; p. 8198
• Main Authors: Xefteris, Vasileios-Rafail, Tsanousa, Athina, Georgakopoulou, Nefeli, Diplaris, Sotiris, Vrochidis, Stefanos, Kompatsiaris, Ioannis
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 26.10.2022; MDPI
• Subjects: Accuracy; Algorithms; Alzheimer's disease; Artificial intelligence; Classification; Datasets; Deep learning; Discriminant analysis; EEG; Electroencephalography; Electromyography; emotion recognition; Emotions; functional connectivity; graph theory; Heart rate; Machine learning; Methods; multimodal fusion; multimodal physiological signals; Neural networks; Physiological aspects; Physiology; Product development; Sensors; Support vector machines
• DOI: 10.3390/s22218198
• ISSN: 1424-8220

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.