Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2BT, United Kingdom.
Centre for Bio-Inspired Technology, Institute of Biomedical Engineering, Imperial College London, London SW7 2AZ, United Kingdom.
J Neural Eng. 2021 Feb 23;18(1). doi: 10.1088/1741-2552/abce3c.
. There has recently been an increasing interest in local field potential (LFP) for brain-machine interface (BMI) applications due to its desirable properties (signal stability and low bandwidth). LFP is typically recorded with respect to a single unipolar reference which is susceptible to common noise. Several referencing schemes have been proposed to eliminate the common noise, such as bipolar reference, current source density (CSD), and common average reference (CAR). However, to date, there have not been any studies to investigate the impact of these referencing schemes on decoding performance of LFP-based BMIs.. To address this issue, we comprehensively examined the impact of different referencing schemes and LFP features on the performance of hand kinematic decoding using a deep learning method. We used LFPs chronically recorded from the motor cortex area of a monkey while performing reaching tasks.. Experimental results revealed that local motor potential (LMP) emerged as the most informative feature regardless of the referencing schemes. Using LMP as the feature, CAR was found to yield consistently better decoding performance than other referencing schemes over long-term recording sessions.. Overall, our results suggest the potential use of LMP coupled with CAR for enhancing the decoding performance of LFP-based BMIs.
. 由于局部场电位 (LFP) 具有信号稳定性和低带宽等理想特性，因此最近人们对其在脑机接口 (BMI) 应用中的应用产生了浓厚的兴趣。LFP 通常是相对于单个单极参考记录的，而这种参考容易受到常见噪声的影响。已经提出了几种参考方案来消除常见噪声，例如双极参考、电流源密度 (CSD) 和公共平均参考 (CAR)。然而，迄今为止，还没有任何研究探讨这些参考方案对基于 LFP 的 BMI 解码性能的影响。. 为了解决这个问题，我们使用深度学习方法全面研究了不同参考方案和 LFP 特征对基于 LFP 的手运动解码性能的影响。我们使用猴子运动皮层区域在执行伸展任务时长期记录的 LFPs。. 实验结果表明，局部运动电位 (LMP) 是最具信息量的特征，无论参考方案如何。使用 LMP 作为特征，与其他参考方案相比，CAR 在长期记录过程中始终能产生更好的解码性能。. 总体而言，我们的结果表明，LMP 与 CAR 相结合具有增强基于 LFP 的 BMI 解码性能的潜力。
