Motor execution reduces EEG signals complexity: Recurrence quantification analysis study.
Motor execution reduces EEG signals complexity: Recurrence quantification analysis study.
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DOI：
10.1063/1.5136246
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发表时间：
2020-02
期刊：
影响因子：
2.9
通讯作者：
E. Pitsik;N. Frolov;K. Hauke Kraemer;V. Grubov;V. Maksimenko;J. Kurths;A. Hramov
中科院分区：
文献类型：
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作者：
E. Pitsik;N. Frolov;K. Hauke Kraemer;V. Grubov;V. Maksimenko;J. Kurths;A. Hramov
开发检测与运动相关的大脑活动的新方法是科学许多方面的关键，特别是在脑机接口应用中。尽管使用传统应用方法揭示了运动相关脑电图的一些众所周知的特征，但它们仍然缺乏运动相关模式的稳健分类。在这里，我们引入了运动相关大脑活动的新特征，并通过考虑感觉运动皮层中μ节律的事件相关去同步化（ERD），即跟踪相应频带中功率谱密度的下降，揭示了潜在神经元动力学的隐藏机制。我们假设运动相关的 ERD 与抑制 μ 带神经元活动的随机波动有关。这是由于参与相应振荡模式的活跃神经元群数量减少所致。在这种情况下，我们预计感觉运动皮层记录的脑电图信号的动态会更加规则，复杂性也会降低。为了支持这一点，我们通过递归量化分析（RQA）来应用信号复杂性的测量。特别是，我们证明某些 RQA 量词对于检测运动开始的时刻非常有用，因此能够对执行运动的侧向性进行分类。
The development of new approaches to detect motor-related brain activity is key in many aspects of science, especially in brain-computer interface applications. Even though some well-known features of motor-related electroencephalograms have been revealed using traditionally applied methods, they still lack a robust classification of motor-related patterns. Here, we introduce new features of motor-related brain activity and uncover hidden mechanisms of the underlying neuronal dynamics by considering event-related desynchronization (ERD) of μ-rhythm in the sensorimotor cortex, i.e., tracking the decrease of the power spectral density in the corresponding frequency band. We hypothesize that motor-related ERD is associated with the suppression of random fluctuations of μ-band neuronal activity. This is due to the lowering of the number of active neuronal populations involved in the corresponding oscillation mode. In this case, we expect more regular dynamics and a decrease in complexity of the EEG signal recorded over the sensorimotor cortex. In order to support this, we apply measures of signal complexity by means of recurrence quantification analysis (RQA). In particular, we demonstrate that certain RQA quantifiers are very useful to detect the moment of movement onset and, therefore, are able to classify the laterality of executed movements.