Paper Title
Artificial Alzheimer’s EEG Dataset Generation Using Generative Adversarial Networks

Alzheimer's disease (AD) is a progressive and irreversible brain disorder that affects memory, thinking, and behavior. It is the leading cause of dementia. However, early diagnosis is critical in increasing the quality and quantity of patient care. The primary method in early diagnosis is electroencephalography (EEG). It has been proposed to assess abnormal brain patterns related to Alzheimer's disease at the cortical level for its low cost, noninvasive, and portability. Furthermore, artificial intelligence tools have been essential in developing models that facilitate disease diagnosis and detection. Deep learning is a promising approach for such applications; however, it requires a reliable dataset. Due to the patient's rights, researchers may not be able to access a sufficient dataset to train the network. This study aims to propose a model to address this issue. A Generative Adversarial Network (GNN) model is presented to generate an artificial EEG dataset for Alzheimer's disease. It may be employed to understand brain processes better and make more accurate medical diagnoses for Alzheimer's disease using deep learning tools. The results show that the proposed model can generate reliable artificial EEG signals for Alzheimer's disease in related channels. Keywords - Alzheimer's Disease(AD), Electroencephalography (EEG), Alzheimer's EEG modelling, Artificial EEG Dataset, Generative Adversarial Network (GAN)