基于提升小波的神经元锋电位并行检测方法

Parallel Detection Method of Neuronal Spikes Based on Lifting Wavelet

神经元动作电位(即锋电位)的实时检测是植入式脑-机接口系统的重要组成环节,为了能够从多通道神经微电极阵列记录的神经信号中实时地检测并提取出神经元的锋电位信息,文中提出了基于提升小波的神经元锋电位检测方法.该方法采用提升小波方法去除了神经信号中的漂移和噪声,然后通过阈值法检测出锋电位信号,最后利用现场可编程门阵列(FPGA)的并行性及流水线结构实现了多通道神经元锋电位的实时并行检测.实验结果显示:和基于个人计算机的检测方法相比,在获得同样检测结果的情况下,文中方法的计算性能有很大的提升,且在单片FPGA上可以实现40个神经通道的并行处理;文中方法不仅可以实现锋电位信号的实时并行检测,而且可以大大提高离线数据处理的效率.

The real-time detection of neuronal action potentials（namely spikes） plays an important role in the implantable brain-computer interface system.In order to implement the real-time detection and extraction of the neuronal spikes from the neural signals recorded by the real-time multi-channel neural microelectrode array,a detection method is proposed based on the lifting wavelet.In this method,the drift and noise of the neural signals are removed by using the lifting wavelet.Then,the neuronal spikes are detected via the threshold setting.Finally,the real-time parallel detection of the multi-channel neuronal spikes is realized by means of the lifting wavelet combining with the parallel and pipelined structures of FPGA.Experimental results show that,as compared with the personal computer-based detection method with the same detection results,the proposed method greatly improves the computational performance and it can achieve the parallel detection of 40 neural channels on a single FPGA chip,and that,the proposed method can not only achieve parallel real-time detection of neuronal spikes but also substantially increase the efficiency of the off-line data processing.