Fugl-Meyer assessment is an accepted method of evaluating motor function for people with stroke. A challenge associated with this assessment is the availability of trained examiners to carry out the evaluation. Neurophysiological biomarkers show promise in addressing the above impediment. Our study investigated the potential of using resting state electroencephalographic (EEG) functional connectivity measures as biomarkers for estimating Fugl-Meyer upper extremity motor score (FMU) in people with chronic stroke. Resting state EEG was recorded from 10 individuals with stroke. Functional connectivity was evaluated through five different processing algorithms and quantified in terms of maximum-coherence between EEG electrodes at 15 frequencies from 1 to 45 Hz. We applied a multi-variate Partial Least Squares (PLS) Correlation analysis to simultaneously identify specific connectivity channels (EEG electrode pairings) and frequencies that robustly correlated with FMU. We then applied PLS-Regression to the identified channels and frequencies to generate a set of coefficients for estimating the FMU. Participants were randomly assigned to a training-set of eight and a test-set of two. Cross-validation with leave-one-out approach on the training-set, using Phase-Lag-Index processing algorithm, resulted in an R ² of 0.97 and a least-square linear fit slope of 1 for predicted versus actual FMU, with a root-mean-square error of 1.9 on FMU scale. Application of regression coefficients to the connectivity measures from the test-set resulted in predicted FMU of 47 and 38 versus actual scores of 46 and 39, respectively. Our results demonstrated that the evaluation of neural correlates of FMU shows promise in addressing the challenges associated with the availability of trained examiners to carry out the assessments.

中文翻译：

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通过功能连接性测度估算Fugl-Meyer上肢运动评分

Fugl-Meyer评估是公认的中风患者运动功能评估方法。与该评估相关的挑战是训练有素的审查员能否进行评估。神经生理生物标志物有望解决上述障碍。我们的研究调查了使用静息状态脑电图（EEG）功能连接性度量作为生物标记物来评估慢性卒中患者Fugl-Meyer上肢运动评分（FMU）的潜力。记录了来自10名中风患者的静息状态EEG。通过五个不同的处理算法对功能连接性进行了评估，并根据从1到45 Hz的15个频率下的EEG电极之间的最大相干性进行了量化。我们应用了多元偏最小二乘（PLS）相关分析，以同时识别与FMU紧密相关的特定连接通道（EEG电极对）和频率。然后，我们将PLS回归应用于所识别的信道和频率，以生成一组用于估计FMU的系数。参加者被随机分配到一组八人的训练组和一组两人的测试套中。使用Phase-Lag-Index处理算法对训练集进行留一法的交叉验证，得出R 参加者被随机分配到一组八人的训练组和一组两人的测试套中。使用Phase-Lag-Index处理算法对训练集进行留一法的交叉验证，得出R 参与者被随机分配到一组八人的训练组和一组两人的测试套中。使用Phase-Lag-Index处理算法对训练集进行留一法的交叉验证，得出R 预测FMU与实际FMU的比值为0.97的²和最小平方线性拟合斜率为1，FMU尺度的均方根误差为1.9。将回归系数应用于测试集的连通性度量后，得出的预测FMU为47和38，而实际得分分别为46和39。我们的结果表明，FMU神经相关性的评估显示出有望解决与训练有素的检查员进行评估相关的挑战。