【摘要】 脑机接口研究中常利用神经电生理信号进行神经信息的解码,但由于小鼠神经电信号(特别是spike)慢性记录较难,目前脑机接口在小鼠上开展较少.本研究记录小鼠初级运动皮层上的局部场电位信号(localfield potential, LFP),通过计算该信号的功率谱密度作为特征输入,采用SVM (support vector machines)分类算法对小鼠压杆运动中的神经信息进行解码.结果发现解码所获得的运动信号与真实信号之间存在一个提前的时间差.为此,我们构建了一个时延SVM模型,利用小鼠上仅有的四通道LFP信号实现了二值运动信号的解码,且时延SVM解码精度更高.更多还原

【Abstract】 In the research of brain-computer interfaces, the neural electrophysiological signals are commonly used for neural information decoding. However, due to the difficulty in recording the neural electrophysiological signals of mice, especially spike, a brain-computer interface is seldom used. In this study, the local field potential(LFP) signals of the primary motor cortex of mice are recorded, and the power spectral density of the LFP is calculated as the input features. The SVM classification algorithm is used to decode the neural information of mice during the lever-pressing movement. The obtained results illustrate that the predicted movement has an early time between the real movement. To this end, a time-delay SVM model is built to decode the binary motion signal for the only four-channel LFP signal of the mice. The result shows that the time-delay SVM decoding achieved high-accuracy performance.更多还原