Minimal Window Duration for Identifying Cognitive Decline Using Movement-Related Versus Rest-State EEG

Until recently, diagnosing people with neurophysiological disorders such as mild cognitive impairment (MCI) was challenging. The common diagnostic techniques used are invasive in nature and time-consuming due to their reliance on the intervention of an expert neuropsychologist and manual diagnosis. Therefore, the adoption of artificial intelligence (AI) and especially machine learning (ML) has proven most useful. It provided healthcare practitioners with an effective tool to diagnose patients faster with higher accuracy. In this paper, a method to separate MCI subjects from healthy controls using movement-related Raw electroencephalogram (EEG) is evaluated, and a new effective EEG segment length is discovered. A variety of binary classifiers are trained and our proposed segment length of 12 s with 50% overlap produces an accuracy of 97.27%.