基于脑电和惯性同步分析的神经动力学耦合研究
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1.燕山大学河北省测试计量技术及仪器重点实验室,秦皇岛 066004;2.燕山大学河北省智能康复及神经调控重点实验室,秦皇岛 066004;3.国家康复辅具研究中心北京市老年功能障碍康复辅助技术重点实验室,北京 100176;4.国家康复辅具研究中心民政部神经功能信息与康复工程重点实验室,北京 100176

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国家自然科学基金(U20A20192, 61901407, 62076216);河北省重点研发计划(21372005D,19214306D);河北省教育厅高等学校科学技术研究项目(QN2019011);燕山大学基础创新科研培育项目(2021LGZD010);河北省创新能力提升计划项目(22567619H)


Research on Neurodynamic Coupling Based on Synchronization Analysis Between EEG and IMU Signals
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1.Key Lab of Measurement Technology and Instrumentation of Hebei Province, Yanshan University, Qinhuangdao 066004, China;2.Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Yanshan University, Qinhuangdao 066004, China;3.Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability, National Research Center for Rehabilitation Technical Aids, Beijing 100176, China;4.Key Laboratory of Neuro-Functional Information and Rehabilitation Engineering of the Ministry of Civil Affairs,National Research Center for Rehabilitation Technical Aids, Beijing 100176, China

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    摘要:

    运动控制是神经、运动和感觉功能的多方面协调及信息交互作用过程,探究运动系统中运动-生理信息间的关联关系对于理解人体运动控制机制具有重要意义。为此,本文通过对脑电信号(Electroencephalogram, EEG)和惯性信息中的加速度信号(Acceleration, ACC)进行相干分析,探究上肢静息态和任务态时EEG和ACC信号间的因果关系及演变规律。首先,通过对7名受试者的EEG和ACC信号进行预处理,去除信号中的干扰成分;进一步,分别计算在静息态、任务态(动态力、静态力)下的EEG和ACC信号间的相干性结果,并通过显著相干的阈值指标来计算显著性面积进而实现量化分析。结果显示,在动态力下的EEG-ACC相干显著性面积大于静态力下的值,静态力下的显著性面积大于静息态下的值;且分别在左、右侧上肢运动时,EEG的C3、C4通道与ACC间的显著性面积也呈现出在对侧运动脑区显著。研究结果表明,EEG和ACC信号间的同步特征在上肢运动的静息态、任务态(动态力、静态力)下有显著特征,这有助于深入理解神经-运动控制机制,为运动功能评估提供新的定量指标,进而为运动功能障碍疾病的早期诊断提供理论依据。

    Abstract:

    Motor control is a process of multifaceted coordination and information interaction among neural, motor and sensory functions. The relationships between motion and physiological information in the motor control system is helpful to understand the mechanism of human motion control. Therefore, to explore the causal relationship and the evolutionary law between electroencephalogram (EEG) and acceleration (ACC) signals during upper limb movement and rest, we apply the coherence method in this study. Firstly, the EEG and ACC signals of 7 subjects are preprocessed to remove the interference components in the signals. Secondly, the coherence values between EEG and ACC signals during the resting, motion-action and motion-maintaining states are calculated respectively, and the significant area is then calculated by the threshold index of significant coherence. The results show that the significant areas in the motion-action state are larger than that of in the motion-maintaining state, and in the motion maintenance state is larger than that of in the resting state. Furthermore, the significant areas between EEG signals of C3 and C4 channels and ACC signals are more significant in the contralateral motor cortex during left and right upper limb movements. These results indicate that there are significant differences between EEG and ACC signals during the resting, motion-action and motion-maintaining states of upper limb movements, which can be helpful to deeply understand the neuromotor control mechanism, and also provide a new quantitative index and the theoretical basis for the assessment of motor function and the early diagnosis of motor dysfunction diseases.

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谢平,蔚建,张腾宇,程生翠,吕岩,陈晓玲.基于脑电和惯性同步分析的神经动力学耦合研究[J].数据采集与处理,2022,37(4):736-746

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  • 收稿日期:2022-05-17
  • 最后修改日期:2022-06-25
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  • 在线发布日期: 2022-08-11