Unravelling Causal Relationships Between Cortex and Muscle with Errors-in-variables Models
Unravelling Causal Relationships Between Cortex and Muscle with Errors-in-variables Models
复制标题
用变量误差模型揭示皮层和肌肉之间的因果关系
DOI：
10.1109/embc46164.2021.9630485
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Guo Z
中科院分区：
文献类型：
--
作者：
Guo Z
皮质肌肉通信通常通过格兰杰因果关系（GC）或定向相干性来估计，目的是评估脑电图（EEG）和肌电图（EMG）信号之间的线性因果关系。然而，在 EEG 和 EMG 信号均存在噪声的情况下，基于标准线性回归 (LR) 模型的传统 GC 可能会被大大低估：一些具有良好运动技能的健康受试者没有表现出明显的 GC。在本研究中，研究了变量误差 (EIV) 模型，目的是估计 GC 背景下的潜在线性时不变系统。使用模拟数据和神经生理学记录评估所提出方法的性能，并与传统 GC 进行比较。结果表明，当使用噪声 EMG 和 EEG 信号检测皮层和外周之间的通信时，基于 EIV 的推断因果关系比典型的基于 LR 的 GC 具有优势。
Corticomuscular communications are commonly estimated by Granger causality (GC) or directed coherence, with the aim of assessing the linear causal relationship between electroencephalogram (EEG) and electromyogram (EMG) signals. However, conventional GC based on standard linear regression (LR) models may be substantially underestimated in the presence of noise in both EEG and EMG signals: some healthy subjects with good motor skills show no significant GC. In this study, errors-in-variables (EIV) models are investigated for the purpose of estimating underlying linear time-invariant systems in the context of GC. The performance of the proposed method is evaluated using both simulated data and neurophysiological recordings, and compared with conventional GC. It is demonstrated that the inferred EIV-based causality offers an advantage over typical LR-based GC when detecting communication between the cortex and periphery using noisy EMG and EEG signals.
影响因子：
5.5
作者：
Baker, SN;Olivier, E;Lemon, RN
通讯作者：
Lemon, RN