Classification of prefrontal and motor cortex signals for three-class fNIRS-BCI
Classification of prefrontal and motor cortex signals for three-class fNIRS-BCI
复制标题
DOI：
10.1016/j.neulet.2014.12.029
复制
发表时间：
2015-02-05
影响因子：
2.5
通讯作者：
Kim, Yun-Hee
中科院分区：
文献类型：
--
作者：
Hong, Keum-Shik;Naseer, Noman;Kim, Yun-Hee
功能近红外光谱(FNIRS)是一种可用于脑-计算机接口(BCI)的光学成像方法。在本研究中，我们同时测量和区分了三种不同的心理活动，即心算(MA)、右手运动表象(RI)和左手运动表象(LI)诱发的近红外信号。10名健康受试者被要求在10名S的任务期间进行MA、RI和LI。使用连续波近红外光谱系统，同时从前额叶和初级运动皮质获取信号。采用多分类线性判别分析对10名受试者的MA、RI和LI进行分类，平均分类准确率为75.6%，在10个S任务期间的2-7s时间窗口内。这些结果证明了使用三种不同的有意生成的认知任务作为输入来实现三类fNIRS-BCI的可行性。(C)2014爱思唯尔爱尔兰有限公司。保留所有权利。
Functional near-infrared spectroscopy (fNIRS) is an optical imaging method that can be used for a brain-computer interface (BCI). In the present study, we concurrently measure and discriminate fNIRS signals evoked by three different mental activities, that is, mental arithmetic (MA), right-hand motor imagery (RI), and left-hand motor imagery (LI). Ten healthy subjects were asked to perform the MA, RI, and LI during a 10 s task period. Using a continuous-wave NIRS system, signals were acquired concurrently from the prefrontal and the primary motor cortices. Multiclass linear discriminant analysis was utilized to classify MA vs. RI vs. LI with an average classification accuracy of 75.6% across the ten subjects, for a 2-7s time window during the a 10 s task period. These results demonstrate the feasibility of implementing a three-class fNIRS-BCI using three different intentionally-generated cognitive tasks as inputs. (C) 2014 Elsevier Ireland Ltd. All rights reserved.