Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, I.R. Iran.
Research Center for Biomedical Technologies and Robotics (RCBTR), Tehran University of Medical Sciences, Tehran, I.R. Iran.
PLoS One. 2022 Jul 1;17(7):e0270757. doi: 10.1371/journal.pone.0270757. eCollection 2022.
Cortico-muscular interactions play important role in sensorimotor control during motor task and are commonly studied by cortico-muscular coherence (CMC) method using joint electroencephalogram-surface electromyogram (EEG-sEMG) signals. As noise and time delay between the two signals weaken the CMC value, coupling difference between non-task sEMG channels is often undetectable. We used sparse representation of EEG channels to compute CMC and detect coupling for task-related and non-task sEMG signals. High-density joint EEG-sEMG (53 EEG channels, 4 sEMG bipolar channels) signals were acquired from 15 subjects (30.26 ± 4.96 years) during four specific hand and foot contraction tasks (2 dynamic and 2 static contraction). Sparse representations method was applied to detect projection of EEG signals on each sEMG channel. Bayesian optimization was employed to select best-fitted method with tuned hyperparameters on the input feeding data while using 80% data as the train set and 20% as test set. K-fold (K = 5) cross-validation method was used for evaluation of trained model. Two models were trained separately, one for CMC data and the other from sparse representation of EEG channels on each sEMG channel. Sensitivity, specificity, and accuracy criteria were obtained for test dataset to evaluate the performance of task-related and non-task sEMG channels detection. Coupling values were significantly different between grand average of task-related compared to the non-task sEMG channels (Z = -6.33, p< 0.001, task-related median = 2.011, non-task median = 0.112). Strong coupling index was found even in single trial analysis. Sparse representation approach (best fitted model: SVM, Accuracy = 88.12%, Sensitivity = 83.85%, Specificity = 92.45%) outperformed CMC method (best fitted model: KNN, Accuracy = 50.83%, Sensitivity = 52.17%, Specificity = 49.47%). Sparse representation approach offers high performance to detect CMC for discerning the EMG channels involved in the contraction tasks and non-tasks.
皮质-肌肉相互作用在运动任务中的感觉运动控制中起着重要作用，通常使用联合脑电图-表面肌电图（EEG-sEMG）信号的皮质-肌肉相干性（CMC）方法进行研究。由于两个信号之间的噪声和时间延迟会削弱 CMC 值，因此通常无法检测到非任务 sEMG 通道之间的耦合差异。我们使用 EEG 通道的稀疏表示来计算 CMC，并检测与任务相关和非任务 sEMG 信号的耦合。从 15 名受试者（30.26±4.96 岁）中采集了高密度联合 EEG-sEMG（53 个 EEG 通道，4 个双极 sEMG 通道）信号，这些受试者在四个特定的手和脚收缩任务（2 个动态收缩和 2 个静态收缩）期间进行收缩。稀疏表示方法用于检测 EEG 信号在每个 sEMG 通道上的投影。贝叶斯优化用于选择在输入馈送数据上具有最佳拟合方法的超参数，同时将 80%的数据作为训练集，20%的数据作为测试集。K 折交叉验证（K=5）方法用于评估训练模型。分别训练了两个模型，一个用于 CMC 数据，另一个用于每个 sEMG 通道的 EEG 通道的稀疏表示。使用测试数据集获得敏感性、特异性和准确性标准，以评估与任务相关和非任务 sEMG 通道检测的性能。与非任务 sEMG 通道相比，任务相关的平均总体平均 CMC 值明显不同（Z=-6.33，p<0.001，任务相关中位数=2.011，非任务中位数=0.112）。即使在单试分析中也发现了强耦合指数。稀疏表示方法（最佳拟合模型：SVM，准确性=88.12%，敏感性=83.85%，特异性=92.45%）优于 CMC 方法（最佳拟合模型：KNN，准确性=50.83%，敏感性=52.17%，特异性=49.47%）。稀疏表示方法提供了高性能，可用于检测 CMC，以区分参与收缩任务和非任务的 EMG 通道。
