Brain-computer interface (BCI) based on functional near-infrared spectroscopy (fNIRS) is a new-type human-computer interaction technique. To explore the separability of fNIRS signals in different motor imageries on the single limb, the study measured the fNIRS signals of 15 subjects (amateur football fans) during three different motor imageries of the right foot (passing, stopping and shooting). And the correlation coefficient of the HbO signal during different motor imageries was extracted as features for the input of a three-classification model based on support vector machines. The results found that the classification accuracy of the three motor imageries of the right foot was 78.89%±6.161%. The classification accuracy of the two-classification of motor imageries of the right foot, that is, passing and stopping, passing and shooting, and stopping and shooting was 85.17%±4.768%, 82.33%±6.011%, and 89.33%±6.713%, respectively. The results demonstrate that the fNIRS of different motor imageries of the single limb is separable, which is expected to add new control commands to fNIRS-BCI and also provide a new option for rehabilitation training and control peripherals for unilateral stroke patients. Besides, the study also confirms that the correlation coefficient can be used as an effective feature to classify different motor imageries.
基于功能性近红外光谱（fNIRS）的脑机接口（BCI）是一种新型的人机交互手段。为探究单个肢体不同运动想象动作 fNIRS 信号的可分性，研究采集了 15 名受试者（业余足球爱好者）在想象右脚三种动作（传球、停球和射门）期间的 fNIRS 信号，提取了不同想象动作期间 HbO 信号的相关系数作为特征，构造了基于支持向量机的三分类模型。试验结果发现：右脚三种想象动作的分类准确率为 78.89%±6.161%；两类运动想象动作的分类，即传球与停球、传球与射门和停球与射门的分类准确率分别为 85.17%±4.768%、82.33%±6.011%、89.33%±6.713%。研究结果表明单个肢体不同运动想象的 fNIRS 具有可分性，这可望为 fNIRS-BCI 增加新的控制命令，也可为单侧中风患者康复训练和控制外设提供一种新的选择。此外，研究也表明相关系数可以作为分类不同想象动作的一种有效特征。.
Keywords: brain-computer interface; classification and recognition; correlation coefficient; functional near-infrared spectroscopy; motor imagery.