Review on EEG-based Dementia Staging using Machine Learning

Dementia has been defined as a deterioration in cognitive capacities which impairs an individual’s capacity to carry out tasks independently daily. It is critical to recognize that dementia is more accurately defined as a syndrome than as a single disease. Electroencephalogram (EEG) is a nonintrusive neural imaging approach, which tracks the brain’s electrical activities. EEG signals have shown notable alterations in dementia patients, making EEG a helpful tool for early-stage dementia recognition and diagnosis. Machine learning algorithms have shown potential in detecting patterns in EEG data that correspond to different stages of dementia. This allows the creation of a unique dementia diagnostic tool that outperforms existing approaches in terms of accuracy and cost-effectiveness. However, further research is needed to evaluate these algorithms in clinical settings. The research dives into the implementation of multiple machine-learning methods, the extraction of attributes from EEG data, and the evaluation of these algorithms’ performance.