Abstract
In brain functional network connectivity analysis, phase synchronization has been effective in detecting regions demonstrating similar dynamics over time. The previously proposed connectivity indices such as phase locking value (PLV), phase lag index (PLI) and weighted phase lag index (WPLI) are widely used. They are, however, influenced by volume conduction or noise. In addition, appropriate thresholds have to be chosen in order to employ them successfully, which leads to uncertainty. In this paper, a novel connectivity index named phase lag based on the Wilcoxon signed-rank test (PLWT) is proposed under the framework of Wilcoxon signed-rank test, which avoids using thresholds to identify effective connections. We analyzed and compared PLWT with previous indices by simulating volume conduction and testing the scale-free character of brain networks constructed based on EEG signals. The experimental results demonstrated that PLWT can be utilized as a reliable and convincing measure to reveal true connections while effectively diminishing the influence of volume conduction.
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Acknowledgment
This work was supported in part by the National Basic Research Program of China under Grant 2015CB351704, the Key Research and Development Plan (Industry Foresight and Common Key Technology) - Key Project of Jiangsu Province under Grant BE2017007-3, and the National Natural Science Foundation of China under Grants 61773114 and 61375118.
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Wu, Y., Gan, J.Q., Wang, H. (2017). Identifying Intrinsic Phase Lag in EEG Signals from the Perspective of Wilcoxon Signed-Rank Test. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10636. Springer, Cham. https://doi.org/10.1007/978-3-319-70090-8_71
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DOI: https://doi.org/10.1007/978-3-319-70090-8_71
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