Cognitive Performance Evaluation in Early Stage of Dementia: A Hybrid EEG/Eye Movement Analysis

Abstract

The number of people around the world with a risk of severe disease from being dementia is increasing significantly. Especially, the elderly, those with preexisting conditions are particularly at risk from the major factor to be changed to an Alzheimer's disease. The condition greatly affects the cognitive functions of the body which will eventually lead to deterioration and deformation of the body to perform regular activities. Traditional approaches have been carried out for identifying the causal factors underlying variations in terms of medical technologies, e.g., the non-invasive imaging methods and noninvasive digital biomarkers using digital signal processing. In this paper, therefore, a noninvasive hybrid approach using electroencephalography (EEG) neurofeedback and eye tracking method has been proposed to identify significant features of an early stage of dementia which later has 80% chance pro-gression of symptoms to Alzheimer's disease. The presence of modern imaging technologies which are expensive and not readily available in terms of temporal resolution response, thus, our proposed noninvasive hybrid EEG/eye tracking analysis proved to be an effective method to classify the control group from cognitively impaired group (or an early stage of dementia) using generalized linear models (GLM) classifier which obtained an average classification accuracy of 89.95%.