A Systematic Study of Role of EEG Mental imagery in Detecting Dementia

Introduction: The Electroencephalography also known as EEG has emerged as a critical means in neurological valuations, particularly in the exposure of dementia-related diseases. EEG-based mental imagery has gained attention for the purpose of diagnosing disease progression monitoring. The paper aims to reconnaissance the claim in the field of Convolutional Neural Networks called CNNs as the technique in detecting dementia from Electroencephalogram (EEG) images. Dementia is a very progressive disorder of neurological disease, categorization done by the advanced deterioration of cognitive function, can be stimulating and very helpful to detect the disorder at initial phases. It explores the area to work of Convolutional Neural Networks in the detection of dementia by analyzing Electroencephalogram (EEG) images. EEG is a non-invasive method or technique which records electrical action that happens in the brain and show probability in detecting neurodegenerative diseases. The practice of deep learning work for the tactics, predominantly CNNs, offers probability in powering, automating and enhancing the research to detect correctness or the truth of dementia disease from EEG signals.