Rahatabad Fereidoun Nowshiravan, Rangraz Parisa, Dalir Masood, Nasrabadi Ali Motie
Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Department of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, Iran.
J Med Signals Sens. 2021 Oct 20;11(4):229-236. doi: 10.4103/jmss.JMSS_47_20. eCollection 2021 Oct-Dec.
Nonlinear dynamics, especially the chaos characteristics, are useful in analyzing bio-potentials with many complexities. In this study, the evaluation of arm-tip force estimation method from the electroencephalography (EEG) signal in the vertical plane has been studied and chaos characteristics, including fractal dimension, Lyapunov exponent, entropy, and correlation dimension characteristics of EEG signals have been measured and analyzed at different levels of forces.
Electromyography signal was recorded with the help of the BIOPEC device (the Mp-100 model) and from the forearm muscle with surface electrodes, and the EEG signals were recorded from five major motor-related cortical areas according to 10-20 standard three times in a normal healthy 33-year-old male, athlete and right handed simultaneously with importing a force to 10 sinkers weighing from 10 to 100 Newton with step 10 Newton.
The findings confirm that force estimation through EEG signals is feasible, especially using fractal dimension feature. The R-squared values for Fractal dimension, Lyapunov exponent, and entropy and correlation dimension features for linear trend line were 0.93, 0.7, 0.86, and 0.41, respectively.
The linear increase of characteristics especially fractal dimension and entropy, together with the results from other EEG and neuroimaging studies, suggests that under normal conditions, brain recruits motor neurons at a linear progress when increasing the force.
非线性动力学，尤其是混沌特性，对于分析具有诸多复杂性的生物电位很有用。在本研究中，对垂直平面内基于脑电图（EEG）信号的臂尖力估计方法进行了评估，并在不同力水平下测量和分析了EEG信号的混沌特性，包括分形维数、李雅普诺夫指数、熵和关联维数特征。
借助BIOPEC设备（Mp - 100型号）并使用表面电极从前臂肌肉记录肌电图信号，在一名33岁健康、右利手的男性运动员中，按照10 - 20标准从五个主要运动相关皮层区域记录EEG信号三次，同时以10牛顿的步长向10个重量从10到100牛顿的重物施加力。
研究结果证实通过EEG信号进行力估计是可行的，尤其是使用分形维数特征。分形维数、李雅普诺夫指数、熵和关联维数特征对于线性趋势线的决定系数（R平方值）分别为0.93、0.7、0.86和0.41。
特征尤其是分形维数和熵的线性增加，以及来自其他EEG和神经影像学研究的结果表明，在正常情况下，大脑在增加力时以线性方式募集运动神经元。
