Abstract
Abstract: In light of recent advancements in eXplainable AI and graph neural networks, our survey paper seeks to analyze their potential in detecting Alzheimer’s disease through EEG datasets. We explore the various EEG datasets available for Alzheimer’s detection, discussing their unique characteristics and sources. We navigate through the landscape of classification methods weighing their pros and cons, and the challenges they present. We then explore attention to graph neural networks (GNNs), assessing their feasibility in Alzheimer’s detection and the potential they hold .We then explore functional connectivity measures and signal processing techniques that can be harnessed to create graphs, offering an in-depth analysis of their appli- cation. Lastly, we tackle the topic of explainability in GNNs, discussing how it can be implemented and evaluated in the context of Alzheimer’s detection. In paper we aim to shed light on the exciting possibilities of applying GNNs in Alzheimer’s detection
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