Steady-state visual evoked potentials (SSVEPs) based brain-computer interface (BCI) has received considerable attention due to its high transfer rate and available quantity of targets. However, the performance of frequency identification methods heavily hinges on the amount of user calibration data and data length, which hinders the deployment in real-world applications. Recently, generative adversarial networks (GANs)-based data generation methods have been widely adopted to create supplementary synthetic electroencephalography (EEG) data, holds promise to address these issues. In this paper, we proposed a GAN-based end-to-end signal transformation network for data length window extension, termed as TEGAN. TEGAN transforms short-time SSVEP signals into long-time artificial SSVEP signals. By incorporating a novel U-Net generator architecture and auxiliary classifier into the network design, the TEGAN could produce conditioned features in the synthetic data. Additionally, to regularize the training process of GAN, we introduced a two-stage training strategy and the LeCam-divergence regularization term during the network implementation. The proposed TEGAN was evaluated on two public SSVEP datasets. With the assistance of TEGAN, the performance of traditional frequency recognition methods and deep learning-based methods have been significantly improved under limited calibration data. This study substantiates the feasibility of the proposed method to extend the data length for short-time SSVEP signals to develop a high-performance BCI system. The proposed GAN-based methods have the great potential of shortening the calibration time for various real-world BCI-based applications, while the novelty of our augmentation strategies shed some value light on understanding the subject-invariant properties of SSVEPs.
翻译:以大脑-计算机界面(BCI)为基础的基于大脑-计算机界面(SSVEPs)的直观定位潜力(SSVEPs)因其高传输率和现有目标数量而得到相当的重视,然而,频率识别方法的性能在很大程度上取决于用户校准数据和数据长度的数量,这妨碍了在现实应用中的部署。最近,基于基因对抗网络(GANs)的数据生成方法被广泛采用,以创建补充合成电脑分析数据(EEEGE),这有希望解决这些问题。在本文中,我们建议为数据超长窗口扩展应用建立一个基于GAN端对端信号转换网络的终端对端信号转换网络。TEGAN将短期SSVEP信号转换成长期人工SSVEP信号。通过在网络设计中纳入新的UNet发电机架构和辅助分类,TEGAN生成了有条件特征。此外,为了规范基于GAN的短期培训过程,我们引入了基于GAN端端对新信号的定位系统(称为TEGAN)的快速信号变换网络应用。TEGAN系统(TEGENAN)的拟议高频度读数据变校准方法,在两个SISSLLAULAULAUI 上大幅数据变校正方法下大幅研算。