Abstract
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.
Abstract (translated)
URL
https://arxiv.org/abs/2301.05599
