A Decision-Making Process Analysis Based on Prefrontal Hemispheric Asymmetry

Decision-making is a complicated cognitive function crucial to social relationships and individual cognition. The prefrontal cortex is implicated in decision-making which is responsible for cognition and behavior. Furthermore, the discrepancy in shape and size causes a difference in the neuronal function of the left and right hemispheres of the brain, known as hemispheric asymmetry, which is taken into account when evaluating decision-making. This work aims to produce a framework for the processing of EEG data to determine the narrow frequency bands that influence the decision-making process. The model utilizes the variable prefrontal asymmetry index and power spectral density features to binary classify the decision-making process based on whether or not there is spatial preference. The accuracy of the SVM classifier, when paired with the Sequentialfs feature selection algorithm, is approximately 66.18%, which is superior to mRmR and Fisher's ratio. The statistical assessment of frequency bands that potentially impact decision-making contributes to applications that demand to forecast decision-making, such as neuromarketing.