Brain Signals Analysis Based Deep Learning Methods: Recent advances in the study of non-invasive brain signals

TL;DR

This paper reviews recent deep learning techniques applied to non-invasive brain signal analysis, highlighting their potential in neurological assessment and decoding strategies.

Contribution

It provides an overview of emerging deep learning methods for analyzing non-invasive brain signals like EEG, MEG, MRI, and CT, emphasizing their application in neurological status determination.

Findings

Deep learning algorithms improve brain signal decoding accuracy

Enhanced neurological status assessment using DL-based analysis

Survey of non-invasive brain signal analysis techniques

Abstract

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.