@article{Lou2026, 
author = {Xicheng Lou and Xinwei Li and Hongying Meng and Peiyang Li and Lisha Zhong and Zhangyong Li},
title = {EEG-GANet: Simulating Electroencephalogram Data to Address Sample Imbalance in P300 Speller},
year = {2026},
journal = {Tsinghua Science and Technology},
volume = {31},
number = {6},
pages = {2877-2891},
keywords = {P300 speller, electroencephalography (EEG), brain−computer interface (BCI), generative adversarial network (GAN)},
url = {https://www.sciopen.com/article/10.26599/TST.2025.9010083},
doi = {10.26599/TST.2025.9010083},
abstract = {The P300 speller is a brain−computer interface (BCI) system that enables character selection using event-related potentials (ERPs). However, the inherent imbalance between target and non-target stimuli biases deep learning models. Reducing non-target samples to mitigate this issue risks underutilizing valuable data. We propose EEG-GANet, a generative adversarial network leveraging domain transformation to augment target samples and mitigate class imbalance in P300 speller datasets. Additionally, we develop EEG-DBNet-V2, a compact dual-branch network that efficiently extracts temporal and spectral features from electroencephalography (EEG) signals, serving both as a classification model and as the discriminator within EEG-GANet. Extensive experiments with ten-fold cross-validation on three public datasets demonstrated EEG-DBNet-V2’s superior classification accuracy compared to state-of-the-art models, achieving this with significantly fewer parameters. Integrating EEG-GANet’s augmented data via fine-tuning further improved classification performance. EEG-GANet effectively addresses sample imbalance by generating physiologically accurate augmented data, substantially enhancing EEG-DBNet-V2’s classification capability. Our study introduces a targeted generative adversarial network (GAN)-based EEG data augmentation framework, enabling balanced model training without discarding valuable non-target samples. The integration of EEG-GANet and EEG-DBNet-V2 offers a lightweight yet robust solution, advancing the practical deployment of EEG-based BCIs under data-constrained conditions. The source code is publicly available at https://github.com/xicheng105/EEG-GANet.}
}