Objective: Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) that can deliver a high information transfer rate (ITR) usually require subject’s calibration data to learn the class- and subject-specific model parameters (e.g. the spatial filters and SSVEP templates). Normally, the amount of the calibration data for learning is proportional to the number of classes (or visual stimuli), which could be huge and consequently lead to a time-consuming calibration. This study presents a transfer learning scheme to substantially reduce the calibration effort. Methods: Inspired by the parameter-based and instance-based transfer learning techniques, we propose a subject transfer based canonical correlation analysis (stCCA) method which utilizes the knowledge within subject and between subjects, thus requiring few calibration data from a new subject. Results: The evaluation study on two SSVEP datasets (from Tsinghua and UCSD) shows that the stCCA method performs well with only a small amount of calibration data, providing an ITR at 198.18±59.12 (bits/min) with 9 calibration trials in the Tsinghua dataset and 111.04±57.24 (bits/min) with 3 trials in the UCSD dataset. Such performances are comparable to those from using the multi-stimulus CCA (msCCA) and the ensemble task-related component analysis (eTRCA) methods with the minimally required calibration data (i.e., at least 40 trials in the Tsinghua dataset and at least 12 trials in the UCSD dataset), respectively. Conclusion: Inter- and intra-subject transfer helps the recognition method achieve high ITR with extremely little calibration effort. Significance: The proposed approach saves much calibration effort without sacrificing the ITR, which would be significant for practical SSVEP-based BCIs.

中文翻译：

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受试者间和受试者间的转移减少了基于SSVEP的高速BCI的校准工作

目标：能够提供高信息传递率（ITR）的基于稳态视觉诱发电位（SSVEP）的脑机接口（BCI）通常需要受试者的校准数据来学习特定于类别和受试者的模型参数（例如空间过滤器和SSVEP模板）。通常，用于学习的校准数据量与类（或视觉刺激）的数量成正比，这可能很大，因此导致耗时的校准。这项研究提出了一种转移学习方案，可以大大减少校准工作。方法：受基于参数和基于实例的迁移学习技术的启发，我们提出了一种基于主题转移的规范相关分析（stCCA）方法，该方法利用了主题内和主题间的知识，因此，几乎不需要来自新受试者的校准数据。结果：对两个SSVEP数据集（来自清华大学和UCSD）的评估研究表明，stCCA方法仅使用少量校准数据即可表现良好，在清华大学进行了9次校准试验，其ITR为198.18±59.12（位/分​​钟）。数据集和111.04±57.24（位/分钟），并在UCSD数据集中进行了3次试验。此类性能与使用最少刺激性校准数据的多重刺激CCA（msCCA）和整体任务相关成分分析（eTRCA）方法的性能相当（例如，清华数据集中的至少40个试验和至少12个试验）在UCSD数据集中）。结论：受试者间和受试者内的转移有助于识别方法以极少的校准工作实现较高的ITR。意义：