Hong Keum-Shik, Naseer Noman, Kim Yun-Hee
School of Mechanical Engineering, Pusan National University, 2 Busandaehak-ro, Geumjeong-gu, Busan 609-735, South Korea; Department of Cogno-Mechatronics Engineering, Pusan National University, 2 Busandaehak-ro, Geumjeong-gu, Busan 609-735, South Korea.
Department of Cogno-Mechatronics Engineering, Pusan National University, 2 Busandaehak-ro, Geumjeong-gu, Busan 609-735, South Korea.
Neurosci Lett. 2015 Feb 5;587:87-92. doi: 10.1016/j.neulet.2014.12.029. Epub 2014 Dec 18.
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 10s 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 10s task period. These results demonstrate the feasibility of implementing a three-class fNIRS-BCI using three different intentionally-generated cognitive tasks as inputs.
功能近红外光谱技术（fNIRS）是一种可用于脑机接口（BCI）的光学成像方法。在本研究中，我们同时测量并区分由三种不同心理活动诱发的fNIRS信号，即心算（MA）、右手运动想象（RI）和左手运动想象（LI）。10名健康受试者被要求在10秒的任务期内执行MA、RI和LI。使用连续波近红外光谱系统，同时从额叶前部和初级运动皮层采集信号。利用多类线性判别分析对MA与RI与LI进行分类，在10秒任务期的2 - 7秒时间窗口内，10名受试者的平均分类准确率为75.6%。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。
