Learning from subject’s calibration data can significantly improve the performance of a steady-state visually evoked potential (SSVEP)-based brain_computer interface (BCI), for example, the state-of-the-art target recognition methods utilize the learned subject-specific and stimulus-specific model parameters. Unfortunately, when dealing with new stimuli or new subjects, new calibration data must be acquired, thus requiring laborious calibration sessions, which becomes a major challenge in developing high-performance BCIs for real-life applications. This study investigates the feasibility of transferring the model parameters (i.e., the spatial filters and the SSVEP templates) across two different groups of visual stimuli in SSVEP-based BCIs. According to our exploration, we can extract a common spatial filter from the spatial filters across different stimulus frequencies and a common impulse response from the SSVEP templates across different neighboring stimulus frequencies, in which the common spatial filter is considered as the transferred spatial filter and the common impulse response is utilized to reconstruct the transferred SSVEP template according to the theory that an SSVEP is a superposition of the impulse responses. Then, we develop a transfer learning canonical correlation analysis (tlCCA) incorporating the transferred model parameters. For evaluation, we compare the recognition performance of the calibration-free, the calibration-based, and the proposed tlCCA on an SSVEP data set with 60 subjects. Experiment results prove that the spatial filters share commonality across different frequencies and the impulse responses share commonality across neighboring frequencies. More importantly, the tlCCA performs significantly better than the calibration-free algorithms, comparably to the calibration-based algorithm. Note to Practitioners—This work is motivated by the long calibration time problem in using an steady-state visually evoked potential (SSVEP)-based brain_computer interface (BCI) because most state-of-the-art frequency recognition methods consider merely the situation that the calibration data and the test data are from the same subject and the same visual stimulus. This article assumes that the model parameters share the stimulus-nonspecific knowledge in a limited stimulus frequency range, and thus, the subject’s old calibration data can be reused to learn new model parameters for new visual stimuli. First, the model parameters can be decomposed into the stimulus-nonspecific knowledge (or subject-specific knowledge) and stimulus-specific knowledge. Second, the new model parameters can be generated via transferring the knowledge across stimulus frequencies. Then, a new recognition algorithm is developed using the transferred model parameters. Experiment results validate the assumptions, and moreover, the proposed scheme could be extended to other scenarios, such as when facing new subjects, or adopting new signal acquisition equipment, which would be helpful to the future development of zero-calibration SSVEP-based BCIs for real-life healthcare applications.

中文翻译：

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在基于 SSVEP 的 BCI 中跨刺激频率传输特定于主题的知识


从受试者的校准数据中学习可以显着提高基于稳态视觉诱发电位（SSVEP）的脑机接口（BCI）的性能，例如，最先进的目标识别方法利用学习到的受试者特定和刺激特定的模型参数。不幸的是，当处理新的刺激或新的受试者时，必须获取新的校准数据，因此需要费力的校准会话，这成为开发用于现实生活应用的高性能BCI的主要挑战。本研究研究了在基于 SSVEP 的 BCI 中跨两组不同的视觉刺激传输模型参数（即空间滤波器和 SSVEP 模板）的可行性。根据我们的探索，我们可以从跨不同刺激频率的空间滤波器中提取公共空间滤波器，并从跨不同相邻刺激频率的 SSVEP 模板中提取公共脉冲响应，其中公共空间滤波器被视为传递的空间滤波器，而根据SSVEP是脉冲响应的叠加的理论，利用公共脉冲响应来重建传输的SSVEP模板。然后，我们开发了包含转移模型参数的转移学习典型相关分析（tlCCA）。为了进行评估，我们在包含 60 名受试者的 SSVEP 数据集上比较了免校准、基于校准和提出的 tlCCA 的识别性能。实验结果证明，空间滤波器在不同频率上具有通用性，并且脉冲响应在相邻频率上具有通用性。 更重要的是，与基于校准的算法相比，tlCCA 的性能明显优于免校准算法。从业者注意事项——这项工作的动机是使用基于稳态视觉诱发电位（SSVEP）的脑机接口（BCI）时存在较长的校准时间问题，因为大多数最先进的频率识别方法仅考虑以下情况：校准数据和测试数据来自同一受试者和相同的视觉刺激。本文假设模型参数在有限的刺激频率范围内共享刺激非特异性知识，因此，可以重用受试者的旧校准数据来学习新视觉刺激的新模型参数。首先，模型参数可以分解为刺激非特定知识（或特定主题知识）和刺激特定知识。其次，可以通过跨刺激频率传递知识来生成新的模型参数。然后，使用传输的模型参数开发新的识别算法。实验结果验证了假设，此外，所提出的方案可以扩展到其他场景，例如面对新的受试者或采用新的信号采集设备时，这将有助于未来开发基于零校准SSVEP的BCI现实生活中的医疗保健应用。