College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, People's Republic of China.
Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, People's Republic of China.
J Neural Eng. 2021 Jun 4;18(4). doi: 10.1088/1741-2552/ac028b.
P300s are one of the most studied event-related potentials (ERPs), which have been widely used for brain-computer interfaces (BCIs). Thus, fast and accurate recognition of P300s is an important issue for BCI study. Recently, there emerges a lot of novel classification algorithms for P300-speller. Among them, discriminative canonical pattern matching (DCPM) has been proven to work effectively, in which discriminative spatial pattern (DSP) filter can significantly enhance the spatial features of P300s. However, the pattern of ERPs in space varies with time, which was not taken into consideration in the traditional DCPM algorithm.In this study, we developed an advanced version of DCPM, i.e. multi-window DCPM, which contained a series of time-dependent DSP filters to fine-tune the extraction of spatial ERP features. To verify its effectiveness, 25 subjects were recruited and they were asked to conduct the typical P300-speller experiment.As a result, multi-window DCPM achieved the character recognition accuracy of 91.84% with only five training characters, which was significantly better than the traditional DCPM algorithm. Furthermore, it was also compared with eight other popular methods, including SWLDA, SKLDA, STDA, BLDA, xDAWN, HDCA, sHDCA and EEGNet. The results showed multi-window DCPM preformed the best, especially using a small calibration dataset. The proposed algorithm was applied to the BCI Controlled Robot Contest of P300 paradigm in 2019 World Robot Conference, and won the first place.These results demonstrate that multi-window DCPM is a promising method for improving the performance and enhancing the practicability of P300-speller.
P300 波是最受研究的事件相关电位（ERP）之一，已被广泛应用于脑机接口（BCI）。因此，快速准确地识别 P300 波是 BCI 研究的一个重要问题。最近，出现了许多用于 P300 拼写器的新型分类算法。其中，判别正则模式匹配（DCPM）已被证明是有效的，其中判别空间模式（DSP）滤波器可以显著增强 P300 波的空间特征。然而，ERP 在空间中的模式随时间而变化，这在传统的 DCPM 算法中并未考虑到。在这项研究中，我们开发了 DCPM 的一个高级版本，即多窗口 DCPM，它包含一系列时变的 DSP 滤波器，以微调空间 ERP 特征的提取。为了验证其有效性，我们招募了 25 名受试者，并要求他们进行典型的 P300 拼写器实验。结果表明，多窗口 DCPM 在仅使用五个训练字符的情况下，字符识别准确率达到了 91.84%，明显优于传统的 DCPM 算法。此外，它还与其他八种流行的方法进行了比较，包括 SWLDA、SKLDA、STDA、BLDA、xDAWN、HDCA、sHDCA 和 EEGNet。结果表明，多窗口 DCPM 表现最好，尤其是在使用小校准数据集时。该算法应用于 2019 年世界机器人大会的 P300 范式脑机接口控制机器人竞赛，并获得第一名。这些结果表明，多窗口 DCPM 是一种很有前途的方法，可以提高 P300 拼写器的性能和增强其实用性。
