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

arXiv: https://arxiv.org/abs/2201.04229 · HTML: https://mlsanity.com/paper/2201.04229/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 55 references that are in the map.
- First posted on arXiv 30 Dec 2021.

## Builds on (most cited first, 4 of 4)

- [Applications of Deep Learning and Reinforcement Learning to Biological Data](https://mlsanity.com/paper/1711.03985/) (2017)
- [Real Time Video based Heart and Respiration Rate Monitoring](https://mlsanity.com/paper/2106.02669/) (2021)
- [Ischemic Stroke Identification Based on EEG and EOG using 1D Convolutional Neural Network and Batch Normalization](https://mlsanity.com/paper/1610.01757/) (2016)
- [High Order Local Directional Pattern Based Pyramidal Multi-structure for Robust Face Recognition](https://mlsanity.com/paper/2012.06838/) (2020)

## Abstract (arXiv)

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.