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Multi-scale order recurrence quantification analysis of EEG signals evoked by manual acupuncture in healthy subjects

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Abstract

To explore the effects of manual acupuncture (MA) on brain activities, we design an experiment that acupuncture at acupoint ST36 of right leg with four different frequencies to obtain electroencephalograph (EEG) signals. Many studies have demonstrated that the complexity of EEG can reflect the states of brain function, so we propose to adopt order recurrence quantification analysis combined with discrete wavelet transform, to analyze the dynamical characteristics of different EEG rhythms under acupuncture, further to explore the effects of MA on the complexity of brain activities from multi-scale point of view. By analyzing the complexity of five EEG rhythms, it is found that the complexity of delta rhythm during acupuncture is lower than before acupuncture, and for alpha rhythm that is higher, but for beta, theta and gamma rhythms there are no obvious changes. All of those effects are especially obvious during acupuncture with frequency of 200 times/min. Furthermore, the determinism extracted from delta, alpha and gamma rhythms can be regarded as a characteristic parameter to distinguish the state acupuncture at 200 times/min and the state before acupuncture. These results can provide a theoretical support for selecting appropriate acupuncture frequency for patients in clinical, and the proposed methods have the potential of exploring the effects of acupuncture on brain activities.

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Acknowledgments

This work was supported by the Key Program of the National Natural Science Foundation of China (Grant No. 50537030), the National Natural Science Foundation of China (Grant No. 61072012 and 61172009), the Young Scientists Fund of the National Natural Science Foundation of China (Grant No. 61104032 and 60901035).

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Correspondence to Jiang Wang.

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Yi, G., Wang, J., Bian, H. et al. Multi-scale order recurrence quantification analysis of EEG signals evoked by manual acupuncture in healthy subjects. Cogn Neurodyn 7, 79–88 (2013). https://doi.org/10.1007/s11571-012-9221-1

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  • DOI: https://doi.org/10.1007/s11571-012-9221-1

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