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

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 g...