Background: An open challenge of P300-based BCI systems focuses on recognizing ERP signals using a reduced number of trials with enough classification rate.

Methods: Three novel methods based on Filter Bank and Canonical Correlation Analysis (CCA) are proposed for the recognition of P300 ERPs using a reduced number of trials. The proposed methods were evaluated with two freely available EEG datasets based on 6x6 speller and were compared with five standard methods: Mean-Amplitude, Step-Wise, Principal Component Analysis, Peak, and CCA.

Results: The proposed methods outperform significantly standard algorithms for P300 identification with a maximum AUC of 0.93 and 0.98, and an average of 0.73 and 0.76, using a single trial.

Conclusion: Proposed methods based on Filter Bank are robust for the identification of P300 using a reduced number of trials, which could be used in real-time BCI spellers for rehabilitation engineering.

背景：基于 P300 的 BCI 系统的一个公开挑战侧重于使用更少的试验次数和足够的分类率来识别 ERP 信号。

方法：提出了三种基于滤波器组和典型相关分析 (CCA) 的新方法，用于使用减少的试验次数识别 P300 ERP。所提出的方法使用两个基于 6x6 拼写器的免费 EEG 数据集进行了评估，并与五种标准方法进行了比较：平均振幅、逐步、主成分分析、峰值和 CCA。

结果：所提出的方法明显优于 P300 识别的标准算法，最大 AUC 为 0.93 和 0.98，平均为 0.73 和 0.76，使用一次试验。

结论：所提出的基于 Filter Bank 的方法通过减少试验次数来识别 P300 是稳健的，可用于康复工程的实时 BCI 拼写器。