PDF(Pubmed)
Abstract：
UNASSIGNED: Brain-computer interfaces (BCIs) are systems that acquire the brain\'s electrical activity and provide control of external devices. Since electroencephalography (EEG) is the simplest non-invasive method to capture the brain\'s electrical activity, EEG-based BCIs are very popular designs. Aside from classifying the extremity movements, recent BCI studies have focused on the accurate coding of the finger movements on the same hand through their classification by employing machine learning techniques. State-of-the-art studies were interested in coding five finger movements by neglecting the brain\'s idle case (i.e., the state that brain is not performing any mental tasks). This may easily cause more false positives and degrade the classification performances dramatically, thus, the performance of BCIs. This study aims to propose a more realistic system to decode the movements of five fingers and the no mental task (NoMT) case from EEG signals.
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).
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).
摘要：
■脑机接口（BCI）是获取大脑电活动并提供外部设备控制的系统。由于脑电图（EEG）是捕获大脑电活动的最简单的非侵入性方法，基于EEG的BCI是非常流行的设计。除了对四肢运动进行分类之外，最近的BCI研究集中在通过采用机器学习技术对同一只手上的手指运动进行分类的准确编码。最先进的研究有兴趣通过忽略大脑的空闲情况来编码五个手指运动(即，大脑不执行任何心理任务的状态)。这可能容易导致更多的误报，并大大降低分类性能，因此，BCI的表现。这项研究旨在提出一种更现实的系统，以从EEG信号中解码五个手指的运动和无心理任务（NoMT）情况。
■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）。
■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）。
