School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, People's Republic of China.
State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, People's Republic of China.
J Neural Eng. 2022 May 12;19(3). doi: 10.1088/1741-2552/ac6b57.
Steady-state visual evoked potential (SSVEP) is an important control method of the brain-computer interface (BCI) system. The development of an efficient SSVEP feature decoding algorithm is the core issue in SSVEP-BCI. It has been proposed to use user training data to reduce the spontaneous electroencephalogram activity interference on SSVEP response, thereby improving the feature recognition accuracy of the SSVEP signal. Nevertheless, the tedious data collection process increases the mental fatigue of the user and severely affects the applicability of the BCI system.A cross-subject spatial filter transfer (CSSFT) method that transfer the existing user model with good SSVEP response to the new user test data without collecting any training data from the new user is proposed.Experimental results demonstrate that the transfer model increases the distinction of the feature discriminant coefficient between the gaze following target and the non-gaze following target and accurately identifies the wrong target in the fundamental algorithm model. The public datasets show that the CSSFT method significantly increases the recognition performance of canonical correlation analysis (CCA) and filter bank CCA. Additionally, when the data used to calculate the transfer model contains one data block only, the CSSFT method retains its effective feature recognition capabilities.The proposed method requires no tedious data calibration process for new users, provides an effective technical solution for the transfer of the cross-subject model, and has potential application value for promoting the application of the BCI system.
稳态视觉诱发电位（SSVEP）是脑机接口（BCI）系统的重要控制方法。开发高效的 SSVEP 特征解码算法是 SSVEP-BCI 的核心问题。已经提出使用用户训练数据来减少自发脑电活动对 SSVEP 响应的干扰，从而提高 SSVEP 信号的特征识别精度。然而，繁琐的数据收集过程增加了用户的精神疲劳，严重影响了 BCI 系统的适用性。
本文提出了一种跨被试空间滤波器传递（CSSFT）方法，该方法可以在不从新用户处收集任何训练数据的情况下，将具有良好 SSVEP 响应的现有用户模型传递到新用户的测试数据中。实验结果表明，该传递模型增加了注视目标和非注视目标之间特征判别系数的区分度，并能在基本算法模型中准确识别错误目标。公共数据集表明，CSSFT 方法显著提高了典型相关分析（CCA）和滤波器组 CCA 的识别性能。此外，当用于计算传递模型的数据仅包含一个数据块时，CSSFT 方法仍然保留其有效的特征识别能力。
本文提出的方法不需要对新用户进行繁琐的数据校准过程，为跨被试模型的传递提供了有效的技术解决方案，对促进 BCI 系统的应用具有潜在的应用价值。
