We are a research team from the School of Computer Science and Engineering, Software Engineering, and Artificial Intelligence at Southeast University, Nanjing, China. We mainly focus on EEG-BCI signal processing and decoding algorithms, and this repository contains most of our preparation works.
The folder EEGNets contains implementations of several classical network architectures [1, 2, 3, 4, 5] using PyTorch, notice that EEG_residual.py is modified based on EEG_deep.py [3], and MultiDecoderEEG.py is a mixed decoding module based on EEG_residual.py and EEG_TCNet.py[4]. train_P300.py and others provide the training and validation framework, using P300 and BCI Competition IV 2a datasets provided by MOABB. The folder results_imgs contains train & test results and data visualizations, and example_usage is a example taht shows how to call a EEGNet in a python script.
Most of our works were conducted on Google Colab, where provides a built-in Jupyter Notebook environment. The file EEG_TCNet.ipynb shows a sample of actual outputs while training.
References:
[1] EEGNet: A compact convolutional neural network for EEG-based brain–computer interfaces
[2] EEG-based brain-computer interface enables real-time robotic hand control at individual finger level
[3] Deep learning with convolutional neural networks for EEG decoding and visualization
[4] EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain–Machine Interfaces
[5] ViT-Based EEG Analysis Method for Auditory Attention Detection