Graph-Attentive CNN for cVEP-BCI with Insights into Electrode Significance (Accompanying repository for our IWANN 2025 submission)
Overview This repository contains the code for the paper:
Graph-Attentive CNN for cVEP-BCI with Insights into Electrode Significance by Milán András Fodor and Ivan Volosyak, submitted to IWANN 2025.
We propose a novel hybrid neural network that combines Convolutional Neural Networks (CNNs) and Graph Attention Networks (GATs) for code-modulated Visual Evoked Potential (cVEP) classification. Our model achieves high validation accuracy (~94%) on a sample-wise classification task while providing insights into the most important EEG electrodes for cVEP decoding.
Key content:
A hybrid GAT-CNN model tailored for cVEP-based BCI systems
Insights into electrode significance using attention coefficients and permutation feature importance
Code for hyperparameter search using Bayesian optimization