ExBrainable: An Open-Source GUI for CNN-based EEG Decoding and Model Interpretation
This provides a user-friendly tool for investigators across disciplines to apply advanced EEG decoding methods, though it is incremental as it focuses on interface development rather than novel algorithms.
The authors tackled the challenge of making CNN-based EEG decoding accessible by developing ExBrainable, an open-source GUI that enables model training, evaluation, and visualization, demonstrating its functions on a motor-imagery EEG dataset with results aligned with neuroscience knowledge.
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.