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首页> 外文期刊>Biomedical and Health Informatics, IEEE Journal of >EEG Activity During Movement Planning Encodes Upcoming Peak Speed and Acceleration and Improves the Accuracy in Predicting Hand Kinematics
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EEG Activity During Movement Planning Encodes Upcoming Peak Speed and Acceleration and Improves the Accuracy in Predicting Hand Kinematics

机译:运动计划期间的脑电活动编码即将出现的峰值速度和加速度,并提高了预测手运动学的准确性

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摘要

The relationship between movement kinematics and human brain activity is an important and fundamental question for the development of neural prosthesis. The peak velocity and the peak acceleration could best reflect the feedforward-type movement; thus, it is worthwhile to investigate them further. Most related studies focused on the correlation between kinematics and brain activity during the movement execution or imagery. However, human movement is the result of the motor planning phase as well as the execution phase and researchers have demonstrated that statistical correlations exist between EEG activity during the motor planning and the peak velocity and the peak acceleration using grand-average analysis. In this paper, we examined whether the correlations were concealed in trial-to-trial decoding from the low signal-to-noise ratio of EEG activity. The alpha and beta powers from the movement planning phase were combined with the alpha and beta powers from the movement execution phase to predict the peak tangential speed and acceleration. The results showed that EEG activity from the motor planning phase could also predict the peak speed and the peak acceleration with a reasonable accuracy. Furthermore, the decoding accuracy of the peak speed and the peak acceleration could both be improved by combining band powers from the motor planning phase with the band powers from the movement execution.
机译:运动学与人脑活动之间的关系是神经假体发展的重要而根本的问题。峰值速度和峰值加速度最能反映前馈型运动。因此,值得进一步研究它们。大多数相关研究集中于运动执行或成像过程中运动学与大脑活动之间的相关性。然而,人体运动是运动计划阶段以及执行​​阶段的结果,研究人员已经证明,使用总体平均分析,在运动计划期间脑电活动与峰值速度和峰值加速度之间存在统计相关性。在本文中,我们检查了脑电图活动的低信噪比是否在试验解码中隐藏了相关性。将运动计划阶段的alpha和beta幂与运动执行阶段的alpha和beta幂相结合,以预测峰值切向速度和加速度。结果表明,从运动计划阶段开始的脑电图活动也可以合理地预测峰值速度和峰值加速度。此外,通过将来自电动机计划阶段的频带功率与来自运动执行的频带功率进行组合,可以提高峰值速度和峰值加速度的解码精度。

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