Sude Pehlivan, AkbugdayCura, Ozlem KarabiberAkbugday, BurakAkan, Aydin2024-11-252024-11-252024978946459361797983315197732076-1465https://doi.org/10.23919/EUSIPCO63174.2024.10714940https://hdl.handle.net/20.500.14365/5615One of the most frequent neurological conditions that impair cognitive abilities and have a major negative impact on quality of life is dementia. In this work, a novel approach for identifying Alzheimer's disease (AD) by utilizing electroencephalogram (EEG) signals via signal processing techniques is proposed. Five spectral domain characteristics are computed for one-minute EEG segment duration using EEG data. Each feature is mapped onto a 9 x 9 matrix called topographic EEG feature maps (EEG-FM) to represent spectral as well as spatial information on the same image. Images were then classified using a 2-layer convolutional neural network (CNN) to classify healthy and AD cases. Results indicate that the constructed CNN generalizes well, and the proposed method can accurately classify AD from EEG-FMs with up to %99 accuracy, precision, and recall with loss values as low as 0.01.eninfo:eu-repo/semantics/closedAccessAlzheimer'S Dementia (Ad)Eeg Feature Maps (Eeg-Fm)Deep LearningCnnConference Object10.23919/EUSIPCO63174.2024.107149402-s2.0-85208436535