Exploring Classification in Open and Closed Eyes EEG Data for People with Cognitive Disorders

Cognitive disorders, including Alzheimer’s Disease (AD), are health issues concerning all society. The evolution of technology and Artificial Intelligence (AI)/ Machine Learning (ML) in the health domain promises an earlier and more accurate diagnosis for Alzheimer’s disease and Dementia. In this study, we examine Healthy patients and patients with AD and Mild Cognitive Impairment (MCI), often a prior step of AD. With the use of EEG, we collect data from their brain activity. After a basic processing step, kernel PCA is applied as a dimensionality reduction method using segments of the multichannel signal, and the transformation output is employed as input for the predictive model. Machine learning functions are used to classify data correctly into Healthy, AD, MCI classes, and a postprocessing step allows for classification at the patient level. The results show that the algorithm can predict with an accuracy of 90 percent and more in total, AD or MCI patients vs. Healthy patients.