Department of Electrical and Computer Engineering from the University of Houston, Houston, TX 77204, United States of America.
J Neural Eng. 2019 Nov 6;16(6):066030. doi: 10.1088/1741-2552/ab4063.
Robotic devices show promise in restoring motor abilities to individuals with upper limb paresis or amputations. However, these systems are still limited in obtaining reliable signals from the human body to effectively control them. We propose that these robotic devices can be controlled through scalp electroencephalography (EEG), a neuroimaging technique that can capture motor commands through brain rhythms. In this work, we studied if EEG can be used to predict an individual's grip forces produced by the hand.
Brain rhythms and grip forces were recorded from able-bodied human subjects while they performed an isometric force production task and a grasp-and-lift task. Grip force trajectories were reconstructed with a linear model that incorporated delta band (0.1-1 Hz) voltage potentials and spectral power in the theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz), low gamma (30-50 Hz), mid gamma (70-110 Hz), and high gamma (130-200 Hz) bands. Trajectory reconstruction models were trained and tested through 10-fold cross validation.
Modest accuracies were attained in reconstructing grip forces during isometric force production (median r = 0.42), and the grasp-and-lift task (median r = 0.51). Predicted trajectories were also analyzed further to assess the linear models' performance based on task requirements. For the isometric force production task, we found that predicted grip trajectories did not yield static grip forces that were distinguishable in magnitude across three task conditions. For the grasp-and-lift task, we estimate there would be an approximate 25% error in distinguishing when a user wants to hold or release an object.
These findings indicate that EEG, a noninvasive neuroimaging modality, has predictive information in neural features associated with finger force control and can potentially contribute to the development of brain machine interfaces (BMI) for performing activities of daily living.
机器人设备在恢复上肢瘫痪或截肢患者的运动能力方面显示出了前景。然而，这些系统仍然受到限制，无法从人体获得可靠的信号来有效控制它们。我们提出，这些机器人设备可以通过头皮脑电图（EEG）来控制，这是一种可以通过脑电波捕获运动指令的神经影像学技术。在这项工作中，我们研究了 EEG 是否可以用于预测个体手部产生的握力。
当健全的人体受试者执行等长力产生任务和抓握-提升任务时，记录脑电波和握力。使用线性模型重建握力轨迹，该模型包含 delta 波段（0.1-1 Hz）电压电势和 theta 波段（4-8 Hz）、alpha 波段（8-13 Hz）、beta 波段（13-30 Hz）、低 gamma 波段（30-50 Hz）、中 gamma 波段（70-110 Hz）和高 gamma 波段（130-200 Hz）的频谱功率。通过 10 折交叉验证训练和测试轨迹重建模型。
在重建等长力产生时的握力（中位数 r = 0.42）和抓握-提升任务（中位数 r = 0.51）方面，达到了适度的准确性。还进一步分析了预测轨迹，以根据任务要求评估线性模型的性能。对于等长力产生任务，我们发现预测的握力轨迹不能产生在三个任务条件下在大小上可区分的静态握力。对于抓握-提升任务，我们估计在区分用户想要握持还是释放物体时，会有大约 25%的误差。
这些发现表明，脑电图作为一种非侵入性的神经影像学模式，具有与手指力控制相关的神经特征的预测信息，并可能有助于开发用于进行日常生活活动的脑机接口（BMI）。
