Key Laboratory for Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China.
IEEE Trans Neural Syst Rehabil Eng. 2013 Mar;21(2):233-43. doi: 10.1109/TNSRE.2013.2243471.
Linear discriminant analysis (LDA) has been widely adopted to classify event-related potential (ERP) in brain-computer interface (BCI). Good classification performance of the ERP-based BCI usually requires sufficient data recordings for effective training of the LDA classifier, and hence a long system calibration time which however may depress the system practicability and cause the users resistance to the BCI system. In this study, we introduce a spatial-temporal discriminant analysis (STDA) to ERP classification. As a multiway extension of the LDA, the STDA method tries to maximize the discriminant information between target and nontarget classes through finding two projection matrices from spatial and temporal dimensions collaboratively, which reduces effectively the feature dimensionality in the discriminant analysis, and hence decreases significantly the number of required training samples. The proposed STDA method was validated with dataset II of the BCI Competition III and dataset recorded from our own experiments, and compared to the state-of-the-art algorithms for ERP classification. Online experiments were additionally implemented for the validation. The superior classification performance in using few training samples shows that the STDA is effective to reduce the system calibration time and improve the classification accuracy, thereby enhancing the practicability of ERP-based BCI.
线性判别分析（LDA）已被广泛应用于脑机接口（BCI）中的事件相关电位（ERP）分类。基于 ERP 的 BCI 要想取得良好的分类性能，通常需要充足的数据记录以有效训练 LDA 分类器，因此系统校准时间较长，这可能会降低系统的实用性并导致用户对 BCI 系统产生抵触情绪。在本研究中，我们引入了一种时空判别分析（STDA）来进行 ERP 分类。作为 LDA 的多向扩展，STDA 方法试图通过从空间和时间维度协同寻找两个投影矩阵，从而最大化目标和非目标类之间的判别信息，从而有效降低判别分析中的特征维数，并显著减少所需的训练样本数量。所提出的 STDA 方法在 BCI 竞赛 III 的数据集 II 和我们自己的实验记录的数据集上进行了验证，并与用于 ERP 分类的最新算法进行了比较。还进行了在线实验以验证该方法。使用少量训练样本即可获得优异的分类性能表明，STDA 可有效减少系统校准时间并提高分类准确性，从而增强基于 ERP 的 BCI 的实用性。
