等长伸膝动作的运动单元放电特征分析

本研究基于表面肌电分解技术,分析伸膝动作中不同发力状态下大腿肌肉运动单元的解码准确性,并对比神经特征和肌电特征在肌肉激活程度估计中的效果. 12名大学生分别以2种发力速度和4种发力等级完成伸膝动作的等长收缩.实验同步采集受试者股内侧肌和股外侧肌处的高密度表面肌电信号和伸膝动作收缩力.基于卷积核补偿算法解码肌电信号得到运动单元动作电位,提取神经特征用于收缩力的互相关分析.结果发现,对于股内侧肌,2种任务及4种收缩力等级下平均解码得到(7±4)个运动单元,股外侧肌平均解码得到(9±5)个运动单元.它们的平均脉冲信噪比(pulse-to-noise ratio,PNR)为30.1 d B,对应解码准确率大于90%.股内侧肌的两种神经特征与力之间的平均相关性分别为(0.79±0.08)和(0.80±0.08),股外侧肌的两种神经特征与力之间的平均相关性分别为(0.85±0.05)和(0.85±0.06).综上可见,基于肌电分解技术可以准确识别不同发力状态下大腿肌肉的运动单元放电活动,并且运动单元放电频率与伸膝动作力高度相关,研究结果可用于运动康复、运动训练及人机接口等领域.

Analysis of Motor Unit Activities Decoded During Knee Isometric Extension

This work aims to characterize the accuracy of decoded motor unit activities during multiple contraction conditions based on electromyography (EMG) decomposition techniques, and to evaluate the performance of extracted neural features for the estimation of muscle activation. Twelve healthy undergraduates participated in the experiments to perform the isometric contraction of knee extension with four levels. The high-density EMG signals were decomposed into motor unit spike trains based on convolution kernel compensation. Two neural features were extracted for the cross-correlation analysis with force. On average, (7±4) motor units were identified from the medial vastus muscle (MVM), while (9±5) motor units were identified from the lateralis vastus muscle (LVM). The average pulse-to-noise ratio (PNR) was 30.1 dB, corresponding to the decomposition accuracy of over 90%. The average correlation coefficient between the two neural features of MVM and the force was (0.79±0.08) and (0.80±0.08), respectively, while the average correlation coefficient of LVM was (0.85±0.05) and (0.85±0.06), respectively. These results demonstrate the feasibility of the identification of motor unit activities under various contraction conditions, and the strong correlation between neural features and force indicates the application of decomposition techniques in rehabilitation, exercise training, and human-machine interfacing.