基于nir的脑机接口运动图像检测的小波分析

Bonkon Koo, H. Vu, Hwan-Gon Lee, Hyung-Cheul Shin, Seungjin Choi
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引用次数: 5

摘要

近红外光谱(NIRS)是一种非侵入性的功能性脑成像设备,用于测量脑活动引起的血流动力学反应。本文收集了与运动意象和休息状态相关的近红外信号,并对这些信号进行了小波分析。据我们所知,小波分析方法虽然是一种广泛使用的时间序列信号分析方法,但尚未被用于运动图像的血流动力学响应。为了探索小波分析的有用性,我们使用各种小波基提取特征,然后通过交叉验证来评估哪些特征更有用。我们的经验结果清楚地表明,小波分析有助于获得有意义的血流动力学响应特征,平均分类准确率约为86%。在我们实验中使用的各种小波基中,离散Meyer小波函数的分类准确率最高,达到93%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Motor imagery detection with wavelet analysis for NIRS-based BCI
Near infrared spectroscopy (NIRS) is a non-invasive functional brain imaging device, which measures hemodynamic responses induced by brain activities. In this paper we collect NIRS signals associated with motor imagery and rest states, and analyze these signals with wavelet analysis. To our best knowledge, wavelet analysis method has not been used for hemodynamic response of motor imagery, although it is one of widely-used time-sequential signal analysis methods. In order to explore the usefulness of wavelet analysis, we extract features using various wavelet bases and then evaluate which features are more useful by cross-validation. Our empirical results clearly indicate that wavelet analysis is useful for obtaining meaningful features of the hemodynamic response, by achieving the averaged classification accuracy of about 86%. Among various wavelet bases used in our experiments, discrete Meyer wavelet function achieved the highest performance with classification accuracy of 93%.
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