Classification of Neurological Disorders from EEG using Deep Learning Frameworks

EEG has been determined to be the optimum method for detecting the majority of regularly occurring brain disorders. The typical EEG based diagnosis for brain disorders is manual and time demanding. The EEG of individuals varies depending on the circumstances surrounding their health. The paper proposes a system for analyzing EEG waves and detecting specified patterns in them in order to diagnose certain biomedical conditions like epilepsy, insomnia, Alzheimer's, and intracranial haemorrhage. For the purpose of the aforementioned goal, ma-chine learning and deep learning algorithms are employed, and a comparison between these models is carried out. Implementation of VI as the front end to the algorithms is also described.