ExBrainable: An Open-Source GUI for CNN-based EEG Decoding and Model Interpretation
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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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 4 of its 27 references that are in the map. First posted on arXiv 10 Jan 2022.
Builds on
4 of 27 references.
References · 4
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2016EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfacescited by 616
- 2019SciPy 1.0--Fundamental Algorithms for Scientific Computing in Pythoncited by 49
- 2018Compact Convolutional Neural Networks for Classification of Asynchronous Steady-state Visual Evoked Potentialscited by 15
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Citing papers per year: 2022: 1.
1 paper.