Steady-state visual evoked potentials (SSVEPs) based brain-computer interface (BCI) has received considerable attention due to its high information transfer rate (ITR) 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 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 Time-window length Extension, termed as TEGAN. TEGAN transforms short-length SSVEP signals into long-length artificial SSVEP signals. By incorporating a novel U-Net generator architecture and an auxiliary classifier into the network architecture, the TEGAN could produce conditioned features in the synthetic data. Additionally, we introduced a two-stage training strategy and the LeCam-divergence regularization term to regularize the training process of GAN during the network implementation. The proposed TEGAN was evaluated on two public SSVEP datasets (a 4-class dataset and a 12-class dataset). 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. And the classification performance gap of various frequency recognition methods has been narrowed. This study substantiates the feasibility of the proposed method to extend the data length for short-time SSVEP signals for developing a high-performance BCI system. The proposed GAN-based methods have the great potential of shortening the calibration time and cutting down the budget for various real-world BCI-based applications.

翻译：基于稳态视觉诱发电位（SSVEP）的脑机接口（BCI）因其高信息传输速率（ITR）和丰富的目标数量而受到广泛关注。然而，频率识别方法的性能严重依赖于用户校准数据的数量和数据长度，这阻碍了其在实际应用中的部署。近年来，基于生成对抗网络（GAN）的数据生成方法被广泛用于合成脑电图（EEG）数据，有望解决这些问题。本文提出了一种基于GAN的端到端信号变换网络用于时间窗长度扩展，称为TEGAN。TEGAN将短时长SSVEP信号转换为长时长人工SSVEP信号。通过将新颖的U-Net生成器架构和辅助分类器整合到网络架构中，TEGAN能够在合成数据中生成条件化特征。此外，我们引入了两阶段训练策略和LeCam散度正则化项，以在网络实现过程中规范GAN的训练过程。所提出的TEGAN在两个公开SSVEP数据集（一个4类别数据集和一个12类别数据集）上进行了评估。借助TEGAN，传统频率识别方法和基于深度学习的方法在有限校准数据下的性能得到了显著提升，并缩小了各种频率识别方法之间的分类性能差距。本研究证实了所提方法扩展短时长SSVEP信号数据长度以开发高性能BCI系统的可行性。所提出的基于GAN的方法在缩短校准时间和降低各类实际BCI应用成本方面具有巨大潜力。