Classification of functional near-infrared spectroscopy signals corresponding to the right- and left-wrist motor imagery for development of a brain-computer interface
Classification of functional near-infrared spectroscopy signals corresponding to the right- and left-wrist motor imagery for development of a brain-computer interface
复制标题
DOI：
10.1016/j.neulet.2013.08.021
复制
发表时间：
2013-10-11
影响因子：
2.5
通讯作者：
Hong, Keum-Shik
中科院分区：
文献类型：
--
作者：
Naseer, Noman;Hong, Keum-Shik
本文介绍了一项关于功能性近红外光谱 (fNIRS) 的研究，表明左右手腕运动想象的血流动力学响应具有不同的模式，可以使用线性分类器进行分类，以开发脑机接口 (BC!)。十名健康的参与者被要求用动觉想象电脑屏幕上显示的右或左手腕弯曲。使用多通道连续波 fNIRS 系统同时采集来自左右初级运动皮层的信号。使用两个不同的特征（氧合血红蛋白浓度变化的平均值和斜率），线性判别分析分类器用于对右手腕和左手腕运动想象进行分类，在 10 秒的任务期间，平均分类准确率分别为 73.35% 和 83.0%。此外，当分析时间限制在整个 10 秒任务周期内的 2-7 秒范围内时，平均分类准确率分别提高到 77.56% 和 87.28%。这些结果证明了基于 fNIRS 的 BCI 的可行性，以及通过去除初始 2 秒跨度和/或峰值后的时间跨度来增强分类器的性能。 (C) 2013 Elsevier Ireland Ltd. 保留所有权利。
This paper presents a study on functional near-infrared spectroscopy (fNIRS) indicating that the hemodynamic responses of the right- and left-wrist motor imageries have distinct patterns that can be classified using a linear classifier for the purpose of developing a brain-computer interface (BC!). Ten healthy participants were instructed to imagine kinesthetically the right- or left-wrist flexion indicated on a computer screen. Signals from the right and left primary motor cortices were acquired simultaneously using a multi-channel continuous-wave fNIRS system. Using two distinct features (the mean and the slope of change in the oxygenated hemoglobin concentration), the linear discriminant analysis classifier was used to classify the right- and left-wrist motor imageries resulting in average classification accuracies of 73.35% and 83.0%, respectively, during the 10 s task period. Moreover, when the analysis time was confined to the 2-7 s span within the overall 10 s task period, the average classification accuracies were improved to 77.56% and 87.28%, respectively. These results demonstrate the feasibility of an fNIRS-based BCI and the enhanced performance of the classifier by removing the initial 2s span and/or the time span after the peak value. (C) 2013 Elsevier Ireland Ltd. All rights reserved.