EVENT-SPECIFIC EEG-FNIRS FEATURE FUSION FOR ALZHEIMER'S DISEASE CLASSIFICATION

Abstract

Alzheimer's disease (AD) remains a significant challenge in neurological disorders, necessitating advanced diagnostic techniques for early detection and intervention. This study presents a novel approach for AD classification utilizing a combination of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals. The distinctive characteristics of the cognitive tasks employed in data acquisition underscore the need for task-specific feature extraction insights. To this end, we propose an innovative event-specific feature extraction method that adapts to the unique attributes of each task and signal. By tailoring feature extraction to the inherent characteristics of each task, we achieve maximal information extraction, thereby elevating classification performance. Our methodology employs Recursive Feature Elimination with Cross-Validation, which progressively deletes features with low importance from the model. This iterative process generates the essential features after the feature extraction. The EEG-fNIRS feature fusion capitalizes on their complementary nature, enhancing the discriminatory power of the classification model. Also, in addition to the resting state data usually used for AD classification, we used data collected through three cognitive tasks to identify rich features of AD patients. Our method demonstrates significant promise in effective diagnosis with varying cognitive statuses - healthy controls, mild cognitive impairment, and AD patients.