Objective: This paper proposes a novel device-to-device transfer-learning algorithm for reducing the calibration cost in a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) speller by leveraging electroencephalographic (EEG) data previously acquired by different EEG systems. Methods: The transferring is done by projecting the scalp-channel EEG signals onto a shared latent domain across devices. Three spatial filtering techniques, including channel averaging, canonical correlation analysis (CCA), and task-related component analysis (TRCA), were employed to extract the shared responses from different devices. The transferred data were integrated into a template-matching-based algorithm to detect SSVEPs. To evaluate its transferability, this paper conducted two sessions of simulated online BCI experiments with ten subjects using 40 visual stimuli modulated by joint frequency-phase coding method. In each session, two different EEG devices were used: first, the Quick-30 system (Cognionics, Inc.) with dry electrodes, and second, the ActiveTwo system (BioSemi, Inc.) with wet electrodes. Results: The proposed method with CCA- and TRCA-based spatial filters achieved significantly higher classification accuracy compared with the calibration-free standard CCA-based method. Conclusion: This paper validated the feasibility and effectiveness of the proposed method in implementing calibration-free SSVEP-based BCIs. Significance: The proposed method has great potentials to enhance practicability and usability of real-world SSVEP-based BCI applications by leveraging user-specific data recorded in previous sessions even with different EEG systems and montages.

中文翻译：

---

通过利用跨设备共享潜在响应促进高速 BCI 拼写器的校准

目标：本文提出了一种新的设备到设备转移学习算法，通过利用先前的脑电图 (EEG) 数据降低基于稳态视觉诱发电位 (SSVEP) 的脑机接口 (BCI) 拼写器的校准成本由不同的 EEG 系统获得。方法：传输是通过将头皮通道 EEG 信号投影到跨设备共享的潜在域来完成的。三种空间滤波技术，包括信道平均、典型相关分析 (CCA) 和任务相关组件分析 (TRCA)，被用来提取来自不同设备的共享响应。传输的数据被集成到基于模板匹配的算法中以检测 SSVEP。为了评估其可转移性，本文使用通过联合频率相位编码方法调制的 40 个视觉刺激，对 10 名受试者进行了两次模拟在线 BCI 实验。在每次会议中，使用两种不同的 EEG 设备：第一个是带有干电极的 Quick-30 系统（Cognionics, Inc.），第二个是带有湿电极的 ActiveTwo 系统（BioSemi, Inc.）。结果：与基于 CCA 的标准方法相比，基于 CCA 和 TRCA 的空间滤波器所提出的方法实现了显着更高的分类精度。结论：本文验证了所提出的方法在实施基于 SSVEP 的无校准 BCI 中的可行性和有效性。意义：