This is the PyTorch implementation of the IFNet architecture for MI-EEG classification.

IFNet aims to explore cross-frequency interactions for enhancing feature representation of MI tasks. Guide by neurophysiological priors and efficient convolution operations, IFNet is capable to extract spectro-spatio-temporally robust features for MI decoding from EEG.

• Set up a virtual environment that meets requirements for code running.

• Organize the original data as the following file structure:

    DatasetDir/A01
                -/training.mat
                -/evaluation.mat
                .
              /A02
                -/training.mat
                -/evaluation.mat
                .
              /...
  

We provide an example of loading BCIC-IV-2A data from original .gdf files. It is showed in dataload.m file.

Specifically, for each .mat file, it contains two items EEG_data and labels with the shape of (C, T, N) and (N,), respectively.

• Configure the file config.py with personalized settings.
• Run the file within_subject.py !

The classification results for IFNet and other competing architectures are as follows:

We also introduce IFNet V2 which yields the highest 79.78% classification accuracy on BCIC-IV-2A. This is currently under research in online settings.

J. Wang, L. Yao and Y. Wang, "IFNet: An Interactive Frequency Convolutional Neural Network for Enhancing Motor Imagery Decoding from EEG," in IEEE Transactions on Neural Systems and Rehabilitation Engineering, doi: 10.1109/TNSRE.2023.3257319.

We thank Mane Ravikiran et al for their wonderful works.

Ravikiran Mane, Effie Chew, Karen Chua, Kai Keng Ang, Neethu Robinson, A.P. Vinod, Seong-Whan Lee, and Cuntai Guan, "FBCNet: An Efficient Multi-view Convolutional Neural Network for Brain-Computer Interface," arXiv preprint arXiv:2104.01233 (2021) https://arxiv.org/abs/2104.01233