Vidaurre C, Schlögl A, Cabeza R, Scherer R, Pfurtscheller G
Department of Electrical and Electronic Engineering, Public University of Navarre, Campus Arrosadia s/n, 31006 Pamplona, Spain.
IEEE Trans Biomed Eng. 2007 Mar;54(3):550-6. doi: 10.1109/TBME.2006.888836.
A study of different on-line adaptive classifiers, using various feature types is presented. Motor imagery brain computer interface (BCI) experiments were carried out with 18 naive able-bodied subjects. Experiments were done with three two-class, cue-based, electroencephalogram (EEG)-based systems. Two continuously adaptive classifiers were tested: adaptive quadratic and linear discriminant analysis. Three feature types were analyzed, adaptive autoregressive parameters, logarithmic band power estimates and the concatenation of both. Results show that all systems are stable and that the concatenation of features with continuously adaptive linear discriminant analysis classifier is the best choice of all. Also, a comparison of the latter with a discontinuously updated linear discriminant analysis, carried out in on-line experiments with six subjects, showed that on-line adaptation performed significantly better than a discontinuous update. Finally a static subject-specific baseline was also provided and used to compare performance measurements of both types of adaptation.
本文介绍了一项对使用各种特征类型的不同在线自适应分类器的研究。对18名未经过训练的健康受试者进行了运动想象脑机接口（BCI）实验。实验采用了三个基于脑电图（EEG）的两类、基于提示的系统。测试了两种连续自适应分类器：自适应二次判别分析和线性判别分析。分析了三种特征类型：自适应自回归参数、对数带功率估计以及两者的串联。结果表明，所有系统都是稳定的，并且将特征与连续自适应线性判别分析分类器进行串联是所有选择中最佳的。此外，在对六名受试者进行的在线实验中，将后者与非连续更新的线性判别分析进行比较，结果表明在线自适应的表现明显优于非连续更新。最后还提供了一个静态的受试者特定基线，并用于比较两种自适应类型的性能测量结果。
