# Analyzing EEG Data with Machine and Deep Learning: A Benchmark

arXiv: https://arxiv.org/abs/2203.10009 · HTML: https://mlsanity.com/paper/2203.10009/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 33 references that are in the map.
- First posted on arXiv 18 Mar 2022.

## Builds on (most cited first, 3 of 3)

- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Multimodal Feature Fusion and Knowledge-Driven Learning via Experts Consult for Thyroid Nodule Classification](https://mlsanity.com/paper/2005.14117/) (2020)
- [Multi-Person Brain Activity Recognition via Comprehensive EEG Signal Analysis](https://mlsanity.com/paper/1709.09077/) (2017)

## Built on by (most cited first, 1 of 1)

- [Early Warning Prediction with Automatic Labeling in Epilepsy Patients](https://mlsanity.com/paper/2310.06059/) (2023)

## Abstract (arXiv)

Nowadays, machine and deep learning techniques are widely used in different areas, ranging from economics to biology. In general, these techniques can be used in two ways: trying to adapt well-known models and architectures to the available data, or designing custom architectures. In both cases, to speed up the research process, it is useful to know which type of models work best for a specific problem and/or data type. By focusing on EEG signal analysis, and for the first time in literature, in this paper a benchmark of machine and deep learning for EEG signal classification is proposed. For our experiments we used the four most widespread models, i.e., multilayer perceptron, convolutional neural network, long short-term memory, and gated recurrent unit, highlighting which one can be a good starting point for developing EEG classification models.