Brain Signals Analysis Based Deep Learning Methods: Recent advances in the study of non-invasive brain signals
It addresses the problem of interpreting brain signals for medical diagnosis, but appears incremental as a review of existing methods.
This paper reviews recent advances in using deep learning algorithms to analyze non-invasive brain signals like EEG and MRI, aiming to decode signals for determining neurological status.
Brain signals constitute the information that are processed by millions of brain neurons (nerve cells and brain cells). These brain signals can be recorded and analyzed using various of non-invasive techniques such as the Electroencephalograph (EEG), Magneto-encephalograph (MEG) as well as brain-imaging techniques such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT) and others, which will be discussed briefly in this paper. This paper discusses about the currently emerging techniques such as the usage of different Deep Learning (DL) algorithms for the analysis of these brain signals and how these algorithms will be helpful in determining the neurological status of a person by applying the signal decoding strategy.