AAC-WGAN: A Novel Attention-Enhanced GAN Framework for SSVEP Augmentation and Classification

The rapid advancement of brain-computer interface (BCI) technology has created interest in steady-state visual evoked potential (SSVEP)-based BCIs, which are valued for their high information transfer rates and ability to manage multiple targets. Nonetheless, the efficacy of SSVEP decoding is often constrained by the volume and duration of user calibration data, limiting its practical application. Generative adversarial networks (GANs) have shown promise in synthesizing SSVEP electroencephalogram (EEG) data. However, they face challenges, such as low signal-to-noise ratio and capturing both temporal and spatial features. To address the need for high-quality data generation, the Attention-Aided Classifier Wasserstein GAN (AAC-WGAN) is proposed, which is a novel GAN model that combines an Attention Mechanism and an Auxiliary Classifier to improve data quality and classification performance. Our experiments on the Direction and Dial datasets reveal that our model performs obviously better, particularly with a training set ratio of 25% synthetic data and 75% real data. It achieves a classification accuracy of 91.65% on the Direction dataset, which is a significant improvement over the baseline accuracy of 84.32% (p =0.008) and outperforms other comparable models (p <0.05 for all comparisons). On the Dial dataset, our model achieves a classification accuracy of 83.48%, outperforming both the baseline of 80.86% and other models. Additional analyses, such as t-SNE visualization and FFT, confirm our model’s effectiveness in feature representation and frequency domain characteristics, with significant improvements in both data quality and classification stability. This work advances the state of SSVEP generation and classification, resulting in significant improvements in data quality and accuracy. It also represents a significant step forward in the development of BCI technologies.