# Deep learning approaches for EEG-based healthcare applications: a comprehensive review

## TL;DR

This paper reviews how deep learning is being used with EEG to improve healthcare applications like diagnosing brain disorders and mental health.

## Contribution

The paper provides a comprehensive review of deep learning approaches for EEG-based healthcare, covering methods, applications, and challenges.

## Key findings

- Deep learning models like CNNs, RNNs, and transformers are being applied to EEG data for healthcare tasks.
- Key challenges include signal variability, limited data availability, and model interpretability.
- The paper highlights emerging trends and research directions to advance EEG-based healthcare applications.

## Abstract

Electroencephalography (EEG) is a longstanding means of non-invasively recording brain signals and has become highly valuable for the study of neurological and cognitive processes. Recent progress in deep learning has also greatly improved both EEG signal analysis and interpretation, making more accurate, reliable and scalable solutions in various healthcare applications. In this review, we present a comprehensive summary of the convergence of EEG and deep learning, with an emphasis on diagnostic of neurological disorders, brain recovery, mental health conditions, and brain-computer interface (BCI) applications. We methodically investigate the application of convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) models, transformer models and hybrid architectures for EEG-based tasks. Key challenges that have been hampering emerging solutions are critically covered, namely signal-related variability, the lack of data, and deep learning model limited interpretability. Finally, we highlight emerging trends, open issues and promising research directions, with the aim of laying a solid ground toward the improvement of EEG-based healthcare applications and to drive future research in this fast-growing research area.