IEEE Trans Biomed Eng. 2020 Aug;67(8):2266-2275. doi: 10.1109/TBME.2019.2958641. Epub 2019 Dec 10.
Event-related potentials (ERPs) are one of the most popular control signals for brain-computer interfaces (BCIs). However, they are very weak and sensitive to the experimental settings including paradigms, stimulation parameters and even surrounding environments, resulting in a diversity of ERP patterns across different BCI experiments. It's still a challenge to develop a general decoding algorithm that can adapt to the ERP diversities of different BCI datasets with small training sets. This study compared a recently developed algorithm, i.e., discriminative canonical pattern matching (DCPM), with seven ERP-BCI classification methods, i.e., linear discriminant analysis (LDA), stepwise LDA, bayesian LDA, shrinkage LDA, spatial-temporal discriminant analysis (STDA), xDAWN and EEGNet for the single-trial classification of two private EEG datasets and three public EEG datasets with small training sets. The feature ERPs of the five datasets included P300, motion visual evoked potential (mVEP), and miniature asymmetric visual evoked potential (aVEP). Study results showed that the DCPM outperformed other classifiers for all of the tested datasets, suggesting the DCPM is a robust classification algorithm for assessing a wide range of ERP components.
事件相关电位（ERPs）是脑机接口（BCIs）中最常用的控制信号之一。然而，它们非常微弱，对实验设置（包括范式、刺激参数甚至周围环境）非常敏感，导致不同 BCI 实验中的 ERP 模式多种多样。开发一种能够适应具有小训练集的不同 BCI 数据集的 ERP 多样性的通用解码算法仍然是一个挑战。本研究比较了一种最近开发的算法，即判别正则模式匹配（DCPM），与七种 ERP-BCI 分类方法，即线性判别分析（LDA）、逐步 LDA、贝叶斯 LDA、收缩 LDA、时空判别分析（STDA）、xDawn 和 EEGNet，用于两个私有 EEG 数据集和三个具有小训练集的公共 EEG 数据集的单次分类。这五个数据集的特征 ERPs 包括 P300、运动视觉诱发电位（mVEP）和微型不对称视觉诱发电位（aVEP）。研究结果表明，DCPM 在所有测试数据集上的表现均优于其他分类器，表明 DCPM 是一种用于评估广泛的 ERP 成分的稳健分类算法。
