Key Laboratory for Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Shanghai, China.
Int J Neural Syst. 2014 Feb;24(1):1450003. doi: 10.1142/S0129065714500038. Epub 2013 Dec 2.
Two main issues for event-related potential (ERP) classification in brain-computer interface (BCI) application are curse-of-dimensionality and bias-variance tradeoff, which may deteriorate classification performance, especially with insufficient training samples resulted from limited calibration time. This study introduces an aggregation of sparse linear discriminant analyses (ASLDA) to overcome these problems. In the ASLDA, multiple sparse discriminant vectors are learned from differently l1-regularized least-squares regressions by exploiting the equivalence between LDA and least-squares regression, and are subsequently aggregated to form an ensemble classifier, which could not only implement automatic feature selection for dimensionality reduction to alleviate curse-of-dimensionality, but also decrease the variance to improve generalization capacity for new test samples. Extensive investigation and comparison are carried out among the ASLDA, the ordinary LDA and other competing ERP classification algorithms, based on different three ERP datasets. Experimental results indicate that the ASLDA yields better overall performance for single-trial ERP classification when insufficient training samples are available. This suggests the proposed ASLDA is promising for ERP classification in small sample size scenario to improve the practicability of BCI.
在脑机接口（BCI）应用中，事件相关电位（ERP）分类的两个主要问题是维度诅咒和偏差方差权衡，这可能会降低分类性能，特别是由于校准时间有限导致训练样本不足。本研究介绍了一种稀疏线性判别分析（ASLDA）的聚合，以克服这些问题。在 ASLDA 中，通过利用 LDA 和最小二乘回归之间的等价性，从不同的 l1-正则化最小二乘回归中学习多个稀疏判别向量，并随后将它们聚合形成一个集成分类器，这不仅可以实现自动特征选择以减轻维度诅咒，还可以降低方差以提高对新测试样本的泛化能力。基于不同的三个 ERP 数据集，在 ASLDA、普通 LDA 和其他竞争的 ERP 分类算法之间进行了广泛的研究和比较。实验结果表明，在训练样本不足的情况下，ASLDA 对单次 ERP 分类具有更好的整体性能。这表明，所提出的 ASLDA 有望在小样本量情况下用于 ERP 分类，以提高 BCI 的实用性。
