# ExBrainable: An Open-Source GUI for CNN-based EEG Decoding and Model Interpretation

arXiv: https://arxiv.org/abs/2201.04065 · HTML: https://mlsanity.com/paper/2201.04065/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 27 references that are in the map.
- First posted on arXiv 10 Jan 2022.

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

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python](https://mlsanity.com/paper/1907.10121/) (2019)
- [Compact Convolutional Neural Networks for Classification of Asynchronous Steady-state Visual Evoked Potentials](https://mlsanity.com/paper/1803.04566/) (2018)

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

- [An overview of open source Deep Learning-based libraries for Neuroscience](https://mlsanity.com/paper/2301.05057/) (2022)

## Abstract (arXiv)

We have developed a graphic user interface (GUI), ExBrainable, dedicated to convolutional neural networks (CNN) model training and visualization in electroencephalography (EEG) decoding. Available functions include model training, evaluation, and parameter visualization in terms of temporal and spatial representations. We demonstrate these functions using a well-studied public dataset of motor-imagery EEG and compare the results with existing knowledge of neuroscience. The primary objective of ExBrainable is to provide a fast, simplified, and user-friendly solution of EEG decoding for investigators across disciplines to leverage cutting-edge methods in brain/neuroscience research.