Development of Proposed Algorithm for Neurometric Index (NI) Based on EEG Signals

Abstract

Nowadays, stress is one of the major issues where too much stress may lead to depression, fatigue and insomnia. Stress can be divided into two types called Eustress and Distress. Eustress or positive stress refers to the positive stress which helps to improve the performance of an individual. In contrast, Distress or negative stress can devastate a person by creating depression and damage the quality of life. It is essential to comprehend and to figure out the state of current stress in the numerical index. This study aims to find a new algorithm which can represent the mental stress condition in the numerical index. A new algorithm has been proposed based on the more established index, Alpha Asymmetry Score (AAS), as a reference. Modifications have been made in term of the frequency band as a variable in the stress index calculation. The classification accuracy of the proposed Stress Asymmetry Score (SAS) is approximately 96% which is 10% higher than AAS. SAS offers larger marginal relative difference at fast beta and slow alpha wave between the right and the left hemisphere, thus, it becomes the best discriminator for mental stress features in EEG classification. The development of the stress index promises a new era of stress brain-related research for future people’s benefit.