Some studies have been motivated by the goal of using EEG to identify cognitive impairment patients with effective algorithms. Lehmann et al. explored the ability of a multitude of linear and nonlinear classification algorithms (i.e., linear discriminant analysis (LDA), neural network (NN), and support vector machine (SVM)) to discriminate between EEG signals of patients with varying degrees of cognitive impairment [9]. Dauwels et al. used LDA and quadratic discriminant analysis (QDA) to classify cognitive impairment [4]. Akrofi et al. studied the classification of cognitive impairment using Gaussian mixture model and selected features from relative average power and the coherence between intrahemispheric channel pairs [3]. Gallego-Jutglà et al. used theta band power and LDA to achieve the best accuracy for diagnosing cognitive impairment [21].
