Emotion Associated Brain Functional Network Analysis in Human Eeg using Graph Measures
With the introduction of modern graph theory concepts in neuroscience, the network perspective study of human
brain has become the most promising in understanding any event processing in the brain. Identification of emotion
associated network patterns and their analysis helps in understanding the emotion processing in the brain. This paper
presents a method to map the emotion with its associated brain functional network and then analyzes the associated networks
by employing graph network measures. The proposed method is applied on emotion data set - `database for emotion analysis
using physiological signals (DEAP)' to form the functional connectivity patterns associated with each emotion using phase
locking value in EEG. The identified emotion associated connectivity patterns and network measures are then employed to
successfully classify multiple emotion events.
Keywords- Emotion, EEG, Phase locking value, Brain functional network, Network measures, Classification.