UNASSIGNED: In this study, a novel praxis for feature extraction is utilized. Using Proper Rotational Components (PRCs) computed through Intrinsic Time Scale Decomposition (ITD), which has been successfully applied in different biomedical signals recently, features for classification are extracted. Subsequently, these features were applied to the inputs of well-known classifiers and their different implementations to discriminate between these six classes. The highest classifier performances obtained in both subject-independent and subject-dependent cases were reported. In addition, the ANOVA-based feature selection was examined to determine whether statistically significant features have an impact on the classifier performances or not.
UNASSIGNED: As a result, the Ensemble Learning classifier achieved the highest accuracy of 55.0% among the tested classifiers, and ANOVA-based feature selection increases the performance of classifiers on five-finger movement determination in EEG-based BCI systems.
UNASSIGNED: When compared with similar studies, proposed praxis achieved a modest yet significant improvement in classification performance although the number of classes was incremented by one (i.e., NoMT).
■在这项研究中,利用了一种新颖的特征提取方法。使用通过固有时间尺度分解(ITD)计算的正确旋转分量(PRCs),最近已成功应用于不同的生物医学信号,提取用于分类的特征。随后,这些特征被应用于众所周知的分类器的输入及其不同的实现,以区分这六个类别。报告了在独立于受试者和依赖受试者的情况下获得的最高分类器性能。此外,检查了基于ANOVA的特征选择,以确定统计上显著的特征是否对分类器性能有影响.
■因此,集成学习分类器在测试分类器中达到了55.0%的最高准确率,和基于ANOVA的特征选择提高了分类器在基于EEG的BCI系统中对五指运动确定的性能。
■与类似研究相比,提出的实践在分类性能上实现了适度但显著的改进,尽管类的数量增加了一个(即,NoMT)。