DESIGNING A SPATIAL NAVIGATION PARADIGM FOR ANALYSING THE EFFECT OF MENTAL OVERLOAD ON MILD COGNITIVE IMPAIRMENT IN ALZHEIMER PATIENTS

Abstract

The research investigates the association between spatial navigation and mental overload among those with Mild Cognitive Impairment (MCI) and the incentive stages of Alzheimer's disease (AD). Employing a multifaceted approach, it combines spatial navigation tasks with a digit numbering working memory challenge to induce mental overload. Electroencephalography (EEG) is utilized to study cognitive effects, particularly employing the Mismatched Negativity (MMN) task to identify states of mental overload and explore changes in cognitive resources allocation in response to auditory stimuli. The central hypothesis posits that the degree of mental overload, reflected in spatial navigation performance, can signal cognitive deterioration during the MCI and early stages of Alzheimer's. The study intends to develop a framework for examining this hypothesis, establishing a system for objectively diagnosing AD during the MCI stage. Three different design approaches, Virtual Reality and Physical world real time navigation paradigms were implemented with consistent modification and enhancement. Analysis of the MMN task includes examining Event Related Potential peaks from EEG data, representing the brain's initial detection and processing of deviant stimuli and variation of these responses to be representative of resource distribution, respectively. Fz, Cz, and Pz signals are primarily used for event-related potentials, with each signifying different cognitive functions such as decision-making, spatial processing, and stress indication. Insights from ERP analysis suggest that EEG post-processing and ERP derivation might be sensitive for real-time navigation paradigms. Further analysis will progress with Time Frequency Analysis of EEG data and enhanced data collection. Previous EEG investigations have consistently revealed neural changes in MCI and AD patients, including changes in alpha, beta, delta, and theta brainwave oscillations, decreased complexity and coherence in EEG recordings, as well as lower ratios of high alpha to low alpha and theta to gamma waves, proposed as potential biomarkers for early AD detection.