IEEE Trans Neural Syst Rehabil Eng. 2014 Mar;22(2):302-11. doi: 10.1109/TNSRE.2014.2303472.
The ability to extract physiological source signals to control various prosthetics offer tremendous therapeutic potential to improve the quality of life for patients suffering from motor disabilities. Regardless of the modality, recordings of physiological source signals are contaminated with noise and interference along with crosstalk between the sources. These impediments render the task of isolating potential physiological source signals for control difficult. In this paper, a novel Bayesian Source Filter for signal Extraction (BSFE) algorithm for extracting physiological source signals for control is presented. The BSFE algorithm is based on the source localization method Champagne and constructs spatial filters using Bayesian methods that simultaneously maximize the signal to noise ratio of the recovered source signal of interest while minimizing crosstalk interference between sources. When evaluated over peripheral nerve recordings obtained in vivo, the algorithm achieved the highest signal to noise interference ratio ( 7.00 ±3.45 dB) amongst the group of methodologies compared with average correlation between the extracted source signal and the original source signal R = 0.93. The results support the efficacy of the BSFE algorithm for extracting source signals from the peripheral nerve.
提取生理源信号以控制各种假肢的能力为患有运动障碍的患者提供了极大的治疗潜力，以提高他们的生活质量。无论采用哪种方式，生理源信号的记录都受到噪声和干扰以及源之间串扰的污染。这些障碍使得隔离用于控制的潜在生理源信号的任务变得困难。在本文中，提出了一种用于控制的新型贝叶斯源滤波器信号提取 (BSFE) 算法。BSFE 算法基于 Champagne 的源定位方法，并使用贝叶斯方法构建空间滤波器，该方法同时最大化感兴趣的恢复源信号的信噪比，同时最小化源之间的串扰干扰。当在体内获得的周围神经记录上进行评估时，与比较组中的其他方法相比，该算法实现了最高的信号噪声干扰比 (7.00 ±3.45 dB)，提取的源信号与原始源信号之间的平均相关系数 R = 0.93。结果支持 BSFE 算法从周围神经中提取源信号的有效性。
