Improving the multi‐class classification of Alzheimer’s disease with machine learning‐based techniques: An EEG‐fNIRS hybridization study

As an incurable neurodegenerative disorder, Alzheimer’s Disease (AD) is being projected to become one of the most expensive diseases for understanding the pathophysiological mechanism. Exploring alterations in the AD brain network is therefore of central importance for early and accurate diagnosis of cognitive deficits levels, yet diagnostic uncertainties exist merely confirmed by postmortem brain examination. The hybrid approach of two modalities, Electroencephalography (EEG) and functional Near‐Infrared Spectroscopy (fNIRS) reveals an ideal technique‐derived diagnosis due to the inexpensive and noninvasive experiments coupled with the reliability and versatility that make it highly desirable for AD multi‐class classification tasks.