Research Center for Brain-Inspired Intelligence, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China.
These authors contributed equally this work.
J Neural Eng. 2022 Apr 4;19(2). doi: 10.1088/1741-2552/ac5eb7.
A rapid serial visual presentation (RSVP)-based brain-computer interface (BCI) is an efficient information detection technology through detecting event-related potentials (ERPs) evoked by target visual stimuli. The BCI system requires a time-consuming calibration process to build a reliable decoding model for a new user. Therefore, zero-calibration has become an important topic in BCI research.In this paper, we construct an RSVP dataset that includes 31 subjects, and propose a zero-calibration method based on a metric-based meta-learning: ERP prototypical matching net (EPMN). EPMN learns a metric space where the distance between electroencephalography (EEG) features and ERP prototypes belonging to the same category is smaller than that of different categories. Here, we employ prototype learning to learn a common representation from ERP templates of different subjects as ERP prototypes. Additionally, a metric-learning loss function is proposed for maximizing the distance between different classes of EEG and ERP prototypes and minimizing the distance between the same classes of EEG and ERP prototypes in the metric space.The experimental results showed that EPMN achieved a balanced-accuracy of 86.34% and outperformed the comparable methods.Our EPMN can realize zero-calibration for an RSVP-based BCI system.
基于快速序列视觉呈现（RSVP）的脑机接口（BCI）是一种通过检测目标视觉刺激引发的事件相关电位（ERP）来进行高效信息检测的技术。BCI 系统需要一个耗时的校准过程来为新用户建立可靠的解码模型。因此，零校准已成为 BCI 研究中的一个重要课题。
在本文中，我们构建了一个包含 31 个被试的 RSVP 数据集，并提出了一种基于基于度量的元学习的零校准方法：ERP 原型匹配网络（EPMN）。EPMN 学习一个度量空间，其中属于同一类别的 EEG 特征与 ERP 原型之间的距离小于不同类别的距离。在这里，我们采用原型学习从不同被试的 ERP 模板中学习一个共同的表示作为 ERP 原型。此外，还提出了一种度量学习损失函数，用于最大化度量空间中不同类 EEG 和 ERP 原型之间的距离，并最小化同一类 EEG 和 ERP 原型之间的距离。
实验结果表明，EPMN 实现了 86.34%的平衡准确率，优于可比方法。
我们的 EPMN 可以实现基于 RSVP 的 BCI 系统的零校准。
