Yeh Chien-Hung, Lo Men-Tzung, Hu Kun
Department of Electrical Engineering, National Central University, Taoyuan City 32001, Taiwan; Research Center for Adaptive Data Analysis, National Central University, Taoyuan City 32001, Taiwan; Medical Biodynamics Program, Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, 221 Longwood Avenue, Boston, MA 02115, USA.
Research Center for Adaptive Data Analysis, National Central University, Taoyuan City 32001, Taiwan; Institute of Translational and Interdisciplinary Medicine and Department of Biomedical Sciences and Engineering, National Central University, Taoyuan City 32001, Taiwan.
Physica A. 2016 Jul 15;454:143-150. doi: 10.1016/j.physa.2016.02.012.
Recent studies of brain activities show that cross-frequency coupling (CFC) play an important role in memory and learning. Many measures have been proposed to investigate the CFC phenomenon, including the correlation between the amplitude envelopes of two brain waves at different frequencies - cross-frequency amplitude-amplitude coupling (AAC). In this short communication, we describe how nonstationary, nonlinear oscillatory signals may produce spurious cross-frequency AAC. Utilizing the empirical mode decomposition, we also propose a new method for assessment of AAC that can potentially reduce the effects of nonlinearity and nonstatonarity and, thus, help to avoid the detection of artificial AACs. We compare the performances of this new method and the traditional Fourier-based AAC method. We also discuss the strategies to identify potential spurious AACs.
近期关于大脑活动的研究表明，交叉频率耦合（CFC）在记忆和学习中发挥着重要作用。人们已经提出了许多方法来研究CFC现象，包括不同频率下两种脑电波的幅度包络之间的相关性——交叉频率幅度-幅度耦合（AAC）。在这篇简短的通讯中，我们描述了非平稳、非线性振荡信号如何产生虚假的交叉频率AAC。利用经验模态分解，我们还提出了一种评估AAC的新方法，该方法有可能减少非线性和非平稳性的影响，从而有助于避免检测到人为的AAC。我们比较了这种新方法与传统基于傅里叶的AAC方法的性能。我们还讨论了识别潜在虚假AAC的策略。
