Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG).

By Christoph Lehmann, Thomas Koenig, V. Jelic, L. Prichep, R. John, L. Wahlund, Y. Dodge, Thomas Dierks. Journal of Neuroscience Methods, 2007

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EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges (2019, methodology): “For example, it has been used for the identification of emotional state from EEG data [129,130], as well as for a classification problem involving the identification of Alzheimer’s from EEG data [131].”
A review on neuroimaging‐based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages (2017, unknown): “For neuroimagingbased AD classification, it remains important to conduct studies comparing between diverse classification algorithms thoroughly, as only limited number of studies have been conducted so far (Khondoker et al., 2016; Lehmann et al., 2007).”
Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests (2011, methodology): “Judging from total accuracy, SVM and RF rank highest amongst the classifiers tested as has been suggested elsewhere [47,48,57,58].”
Diagnosis of Alzheimer's disease from EEG signals: where are we standing? (2010, background): “, [156, 163]), it is hard to compare existing studies and to verify whether results are consistent.”