Abstract: In understanding brain functioning by Electroencephalography (EEG), it is essential to be able to not only identify more active brain areas but also understand connectivity among different areas. The functional and efficient connectivity networks of the brain have been examined in this study by constructing a connectivity feature map (CFM) with four widely used connectivity methods from the Database for Emotion Analysis Using Physiological Signals (DEAP) emotional EEG data to research how this connectivity's patterns are influenced by emotion. According to the investigation results, emotions are mainly related to the parietal, central, and frontal regions. The parietal region is more responsible for emotion alteration among these three regions. Positive emotions are associated with more direct correlations and dependencies than negative ones. When experiencing negative emotions, the regions of the brain function more synchronously as well as there are less flow of information. Whether direct or inverse, there is less correlation between brain regions in the higher frequency band than in the lower frequency band. Higher frequencies are associated with increased dependence and directed information transfer between brain regions. Generally, the electrodes in the same lobe show stronger connectivity than those in different lobes. At a glance, the present study is a comprehensive analysis to understand brain network stimulation for emotion from EEG, and it significantly differs from the existing emotion recognition studies typically focused on recognition proficiency.

Mahfuza Akter Maria, M. A. H. Akhand and Md Abdus Samad Kamal, “Understanding Brain Network Stimulation for Emotion Analyzing Connectivity Feature Map from Electroencephalography” International Journal of Advanced Computer Science and Applications(IJACSA), 16(4), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160455

@article{Maria2025,
title = {Understanding Brain Network Stimulation for Emotion Analyzing Connectivity Feature Map from Electroencephalography},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160455},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160455},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {4},
author = {Mahfuza Akter Maria and M. A. H. Akhand and Md Abdus Samad Kamal}
}