As a relatively new physiological signal of brain, functional near-infrared spectroscopy (fNIRS) is being used more and more in brain–computer interface field, especially in the task of motor imagery. However, the classification accuracy based on this signal is relatively low. To improve the accuracy of classification, this paper proposes a new experimental paradigm and only uses fNIRS signals to complete the classification task of six subjects. Notably, the experiment is carried out in a non-laboratory environment, and movements of motion imagination are properly designed. And when the subjects are imagining the motions, they are also subvocalizing the movements to prevent distraction. Therefore, according to the motor area theory of the cerebral cortex, the positions of the fNIRS probes have been slightly adjusted compared with other methods. Next, the signals are classified by nine classification methods, and the different features and classification methods are compared. The results show that under this new experimental paradigm, the classification accuracy of 89.12% and 88.47% can be achieved using the support vector machine method and the random forest method, respectively, which shows that the paradigm is effective. Finally, by selecting five channels with the largest variance after empirical mode decomposition of the original signal, similar classification results can be achieved.

功能性近红外光谱法（fNIRS）作为一种相对较新的大脑生理信号，正在越来越多地用于脑机接口领域，尤其是在运动成像领域。但是，基于该信号的分类精度相对较低。为了提高分类的准确性，本文提出了一种新的实验范式，仅使用fNIRS信号来完成六个主题的分类任务。值得注意的是，实验是在非实验室环境中进行的，运动想象力的运动得到了适当的设计。而且，当被摄对象想像动作时，他们也在模仿动作以防止分心。因此，根据大脑皮层的运动区理论，与其他方法相比，fNIRS探针的位置已稍作调整。接下来，通过九种分类方法对信号进行分类，并比较不同的特征和分类方法。结果表明，在这种新的实验范式下，使用支持向量机方法和随机森林法分别可以达到89.12％和88.47％的分类精度，表明该范式是有效的。最后，通过在原始信号的经验模式分解之后选择方差最大的五个通道，可以实现相似的分类结果。使用支持向量机方法和随机森林方法分别可以达到47％，这表明该范例是有效的。最后，通过在原始信号的经验模式分解之后选择方差最大的五个通道，可以实现相似的分类结果。使用支持向量机方法和随机森林方法分别可以达到47％，这表明该范例是有效的。最后，通过在原始信号的经验模式分解之后选择方差最大的五个通道，可以实现相似的分类结果。