REVIEW ON COGNITIVE DISORDER IDENTIFICATION WITH EEG SIGNALS USING IMAGE AND VIDEO PROCESSING

This survey looks at the cutting-edge use of image and video processing methods to detect cognitive impairments using electroencephalogram (EEG) signals. Appropriate treatment for these types of disorders depends on early detection and observation. Scientists and researchers can evaluate patterns of brain activity to find the type of cognitive disorder by applying well-established image and video processing techniques on EEG datasets. Conventional EEG analysis can be laborious and subjective because it frequently depends on manual interpretation. The application of image and video processing techniques along with machine learning and deep learning algorithms, has made it possible in the conversion of EEG signals into visual representations, which are highlighted in this study. This work examines the latest developments in converting raw EEG that states the position of electrodes and information from each electrode that are utilized with image and video processing techniques like pattern recognition, deep learning, and feature extraction. The working of such methods are compared to improve the categorization and visualization of EEG patterns linked to different types of cognitive impairments. The study also discusses the current issues, such as the requirement for strong validation frameworks, noise interference, and data harmonization. This survey attempts to highlight the possibility of combining picture and video processing with EEG analysis to enhance diagnostic precision and enable prompt treatments in cognitive healthcare by consolidating current studies.