低频运动皮层脑电图预测四种力量发展速度

IF 2.4 3区 计算机科学 Q2 COMPUTER SCIENCE, CYBERNETICS IEEE Transactions on Haptics Pub Date : 2024-07-15 DOI:10.1109/TOH.2024.3428308
Rory O'Keeffe, Seyed Yahya Shirazi, Alessandro Del Vecchio, Jaime Ibanez, Natalie Mrachacz-Kersting, Ramin Bighamian, John-Ross Rizzo, Dario Farina, S Farokh Atashzar
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引用次数: 0

摘要

运动相关皮质电位(MRCP)是脑电图(EEG)信号的低频成分,源自运动皮质和周围皮质区域。由于 MRCP 反映了运动控制的意图和执行情况,因此有可能成为患者与神经康复机器人之间的交流界面。在这项研究中,我们研究了以 Cz 电极为中心记录的脑电信号,目的是解码胫骨前肌等长收缩时的四种力量发展速率(RFD)。相对于肌肉的最大自主收缩(MVC),RFD 的四个级别定义如下:慢速(20% MVC/s)、中速(30% MVC/s)、快速(60% MVC/s)和弹道(120% MVC/s)。在分类过程中,对描述脑电图踪迹的三个特征集进行了评估。其中包括(i) δ 波段的 MRCP 形态特征,如时间和振幅;(ii) δ 波段的 MRCP 统计特征,如标准偏差、平均值和峰度;以及 (iii) 0.1-90 Hz 范围内的宽带时频特征。利用支持向量机对 RFD 的四个级别进行了准确分类。使用宽带时频特征时,准确率为 83% ± 9%(平均值 ± SD)。同时,使用 MRCP 统计特征时,准确率为 78%±12%(平均值±标度)。对 MRCP 波形的分析表明,它包含了有关计划、执行、完成和持续时间的信息量很大的数据。时间分析强调了δ波段在转化为运动指令方面的重要性,这对神经工程系统领域具有广阔的前景。
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Low-frequency Motor Cortex EEG Predicts Four Rates of Force Development.

The movement-related cortical potential (MRCP) is a low-frequency component of the electroencephalography (EEG) signal that originates from the motor cortex and surrounding cortical regions. As the MRCP reflects both the intention and execution of motor control, it has the potential to serve as a communication interface between patients and neurorehabilitation robots. In this study, we investigated the EEG signal recorded centered at the Cz electrode with the aim of decoding four rates of force development (RFD) during isometric contractions of the tibialis anterior muscle. The four levels of RFD were defined with respect to the maximum voluntary contraction (MVC) of the muscle as follows: Slow (20% MVC/s), Medium (30% MVC/s), Fast (60% MVC/s), and Ballistic (120% MVC/s). Three feature sets were assessed for describing the EEG traces in the classification process. These included: (i) MRCP Morphological Characteristics in the δ-band, such as timing and amplitude; (ii) MRCP Statistical Characteristics in the δ-band, such as standard deviation, mean, and kurtosis; and (iii) Wideband Time-frequency Features in the 0.1-90 Hz range. The four levels of RFD were accurately classified using a support vector machine. When utilizing the Wideband Time-frequency Features, the accuracy was 83% ± 9% (mean ± SD). Meanwhile, when using the MRCP Statistical Characteristics, the accuracy was 78% ± 12% (mean ± SD). The analysis of the MRCP waveform revealed that it contains highly informative data on the planning, execution, completion, and duration of the isometric dorsiflexion task. The temporal analysis emphasized the importance of the δ-band in translating to motor command, and this has promising implications for the field of neural engineering systems.

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来源期刊
IEEE Transactions on Haptics
IEEE Transactions on Haptics COMPUTER SCIENCE, CYBERNETICS-
CiteScore
5.90
自引率
13.80%
发文量
109
审稿时长
>12 weeks
期刊介绍: IEEE Transactions on Haptics (ToH) is a scholarly archival journal that addresses the science, technology, and applications associated with information acquisition and object manipulation through touch. Haptic interactions relevant to this journal include all aspects of manual exploration and manipulation of objects by humans, machines and interactions between the two, performed in real, virtual, teleoperated or networked environments. Research areas of relevance to this publication include, but are not limited to, the following topics: Human haptic and multi-sensory perception and action, Aspects of motor control that explicitly pertain to human haptics, Haptic interactions via passive or active tools and machines, Devices that sense, enable, or create haptic interactions locally or at a distance, Haptic rendering and its association with graphic and auditory rendering in virtual reality, Algorithms, controls, and dynamics of haptic devices, users, and interactions between the two, Human-machine performance and safety with haptic feedback, Haptics in the context of human-computer interactions, Systems and networks using haptic devices and interactions, including multi-modal feedback, Application of the above, for example in areas such as education, rehabilitation, medicine, computer-aided design, skills training, computer games, driver controls, simulation, and visualization.
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