@inproceedings{
hsieh2023xbrainlab,
title={{XB}rainLab: An Open-Source Software for Explainable Artificial Intelligence-Based {EEG} Analysis},
author={Chia-Ying Hsieh and Jing-Lun Chou and Yu-Hsin Chang and Chun-Shu Wei},
booktitle={NeurIPS 2023 AI for Science Workshop},
year={2023},
url={https://openreview.net/forum?id=82brfaM02h}
}
Recent advancements in explainable artificial intelligence have significantly accelerated scientific discoveries across various fields. In the realm of neuroscience research, the application of deep interpretation techniques has yielded valuable insights into brain functioning and mechanisms. We introduce XBrainLab, an accessible EEG analysis tool featuring a user-friendly graphical user interface (GUI) seamlessly compatible with code scripting. XBrainLab offers a comprehensive, end-to-end deep learning EEG analysis pipeline, capable of converting raw EEG signals into comprehensible visualizations of neural patterns. Through practical demonstrations using diverse EEG datasets, we highlight XBrainLab's versatility in interpreting neural representations in alignment with established neuroscience knowledge. This evolving open-source platform bridges cutting-edge computational techniques with the forefront of neuroscientific research. The code repository can be accessed at https://github.com/CECNL/XBrainLab.
XBrainLab: An Open-Source Software for Explainable Artificial Intelligence-Based EEG Analysis
Chia-Ying Hsieh Jing-Lun Chou Yu-Hsin Chang Chun-Shu Wei