EEG-Based Discrimination Between Patients with MCI and Alzheimer

There is a high similarity between the signs and symptoms of patients with Alzheimer and those with mild cognitive impairment (MCI). Although several attempts have been made to differentiate these two groups of patients by decoding the fluctuation of their electroencephalogram (EEG), the achieved results are not yet promising. To increase the differentiation rate, in this study, 14 patients with Alzheimer from 13 patients with MCI have been voluntarily enrolled while their EEG signals are recorded in presence of visual stimuli. To suppress the disrupting artifacts and noises (e.g., eye-blink and movement artefact) from the recorded EEGs, independent component analysis is applied. Next, the visual evoke potential (VEP) patterns are extracted by synchronous averaging and then multi-linear principal component analysis (MPCA) is applied to elicit discriminative features from VEPs of the patients. After feature extraction by MPCA, the reduced feature vectors of both groups are applied to a nearest neighbor classifier, leading to 77.35% differentiation accuracy.