Abstract:
Stress is a common phenomenon that affects individuals from all walks of life and it is associated
with physical and physiological illness. Brain plays a key role in determining stress. This study
represents the comprehensive framework for analyzing EEG data to investigate the connectivity and
network dynamics under stress and normal. Our primary interest is how classifying stress in network
so this is assessed through effective connectivity in which we use partial directed coherence to
measure how one region influences other. Connectivity networks are computed using adjacency
matrix over time and subjected to graph theoretical analysis to focus on degree and betweenness
Degree highlighted highly connected region and betweenness revealed brain central region that
facilitate global communication across different brain region. Further analysis in microstate that
demonstrate the distinct patterns in brain, from this analysis microstate 4 show high duration and
transition in normal conditions, while microstate 5 show high duration and transition in stress
condition. Microstate features are used for classification into stress and normal by using three
machine learning classifiers: random forest (RF), K-nearest neighbors (KNN) and support vector
machine (SVM). In the classification analysis, Random Forest achieved 94% accuracy followed by
KNN and SVM. Furthermost, this approach provides valuable insights into brain connectivity and
demonstrate the utility of microstate features in stress classification.
