Regularizing multi-bands Common Spatial Patterns (RMCSP): A data processing method for brain-computer interface

Le Quoc Thang, C. Temiyasathit
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引用次数: 5

Abstract

In this paper, we propose a novel approach which is called the Regularizing Multi-bands Common Spatial Patterns (RMCSP) that particularly used for processing motor-imagery based Electroencephalography (EEG) data in Brain-computer Interface (BCI). The usage of BCI is severely limited due to the inconvenience of large number of channels used in recording devices. Moreover, Common Spatial Patterns (CSP) is a very well-known algorithm for its efficiency, but it just can extract the spatial information of the brain signals. To address these issues, we introduce the RMCSP method that exploits data in spectral, temporal and spatial domains in order to increase the classification accuracy in BCI. In addition, RMCSP is designed to handle EEG with small number of channels. To verify the efficacy of our approach, we rigorously tested the performances of the method in 17 subjects, from BCI competition datasets, in both two-class and four-class problems. Results show that RMCSP approach can outperform normal CSP method by nearly 10% in terms of median classification accuracy. It also enables us to significantly reduce the number of channels used in the datasets without decreasing the performances of the subjects.
正则化多波段公共空间模式(RMCSP):一种脑机接口数据处理方法
在本文中,我们提出了一种新的方法,称为正则化多波段公共空间模式(RMCSP),特别用于脑机接口(BCI)中基于运动图像的脑电图(EEG)数据的处理。由于记录设备中使用的大量通道的不便,BCI的使用受到严重限制。此外,公共空间模式(Common Spatial Patterns, CSP)算法以其高效而闻名,但它只能提取大脑信号的空间信息。为了解决这些问题,我们引入了RMCSP方法,该方法利用光谱、时间和空间域的数据来提高脑机接口的分类精度。此外,RMCSP被设计用于处理少量通道的EEG。为了验证我们方法的有效性,我们在来自BCI竞赛数据集的17个主题中严格测试了该方法在两类和四类问题中的性能。结果表明,RMCSP方法的中位数分类准确率比常规CSP方法高出近10%。它还使我们能够在不降低主题性能的情况下显着减少数据集中使用的通道数量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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