This study addresses the significant challenge of developing efficient decoding algorithms for classifying steady-state visual evoked potentials (SSVEPs) in scenarios characterized by extreme scarcity of calibration data, where only one calibration is available for each stimulus target. To tackle this problem, we introduce a novel cross-subject dual-domain fusion network (CSDuDoFN) incorporating task-related and task-discriminant component analysis (TRCA and TDCA) for one-shot SSVEP classification. The CSDuDoFN framework is designed to comprehensively transfer information from source subjects, while TRCA and TDCA are employed to exploit the single available calibration of the target subject. Specifically, we develop multi-reference least-squares transformation (MLST) to map data from both source subjects and the target subject into the domain of sine-cosine templates, thereby mitigating inter-individual variability and benefiting transfer learning. Subsequently, the transformed data in the sine-cosine templates domain and the original domain data are separately utilized to train a convolutional neural network (CNN) model, with the adequate fusion of their feature maps occurring at distinct network layers. To further capitalize on the calibration of the target subject, source aliasing matrix estimation (SAME) data augmentation is incorporated into the training process of the ensemble TRCA (eTRCA) and TDCA models. Ultimately, the outputs of the CSDuDoFN, eTRCA, and TDCA are combined for SSVEP classification. The effectiveness of our proposed approach is comprehensively evaluated on three publicly available SSVEP datasets, achieving the best performance on two datasets and competitive performance on one. This underscores the potential for integrating brain-computer interface (BCI) into daily life.

翻译：本研究针对在标定数据极度匮乏的场景下（每个刺激目标仅有一个标定样本）开发高效稳态视觉诱发电位（SSVEP）解码算法这一重大挑战。为解决该问题，我们提出一种新颖的跨被试双域融合网络（CSDuDoFN），该网络融合任务相关成分分析（TRCA）与任务判别成分分析（TDCA）用于一次性SSVEP分类。CSDuDoFN框架旨在全面迁移源被试的信息，同时利用TRCA和TDCA挖掘目标被试的单个可用标定样本。具体而言，我们开发了多参考最小二乘变换（MLST）将源被试与目标被试的数据映射至正弦-余弦模板域，从而降低个体间差异性并促进迁移学习。随后，分别在正弦-余弦模板域的变换数据与原始域数据上训练卷积神经网络（CNN）模型，并在不同网络层实现特征图的充分融合。为进一步利用目标被试的标定信息，在集成TRCA（eTRCA）与TDCA模型的训练过程中引入源混叠矩阵估计（SAME）数据增强方法。最终，融合CSDuDoFN、eTRCA及TDCA的输出结果进行SSVEP分类。我们在三个公开SSVEP数据集上全面验证了所提方法的有效性，在两个数据集中取得最优性能，在另一个数据集中获得具有竞争力的表现。这凸显了将脑机接口（BCI）融入日常生活的潜力。