IEEE Trans Biomed Eng. 2024 Apr;71(4):1332-1344. doi: 10.1109/TBME.2023.3334379. Epub 2024 Mar 20.
In this paper, a novel extended form of multivariate variational mode decomposition (MVMD) method to multigroup data named as grouped MVMD (GMVMD) is proposed. GMVMD is distinct from MVMD as it extracts common frequencies with strong correlations among regional channels.
Firstly, GMVMD utilizes a new clustering algorithm named as frequencies grouping algorithm to classify the nearest common frequencies among all channels to specified groups. Secondly, a generic variational optimization model which is extended from MVMD is formulated. Thirdly, alternating direction method of multipliers (ADMM) is utilized to obtain optimal solution of GMVMD model.
The proposed method introduces an extra parameter to decide the number of clusterings which need to be specified by the user. The effectiveness and superiority of the algorithm are demonstrated on a series of experiments. The utility of GMVMD is verified by grouping real-world electroencephalogram (EEG) data having similar center frequencies successfully.
GMVMD outperforms MVMD in mode-alignment, signal reduction error and et al. Significance: GMVMD can obtain more accurate center frequencies and less signal reduction error than MVMD.
本文提出了一种新的多变量变分模态分解（MVMD）方法的扩展形式，用于多组数据，称为分组 MVMD（GMVMD）。GMVMD 与 MVMD 不同，因为它可以提取具有区域通道之间强相关性的共同频率。
首先，GMVMD 使用一种新的聚类算法，称为频率分组算法，将所有通道之间的最近共同频率分类到指定的组中。其次，构建了一个从 MVMD 扩展而来的通用变分优化模型。然后，利用增广拉格朗日乘子法（ADMM）来获得 GMVMD 模型的最优解。
该方法引入了一个额外的参数来确定聚类的数量，这需要用户指定。通过一系列实验验证了该算法的有效性和优越性。通过成功分组具有相似中心频率的真实脑电（EEG）数据，验证了 GMVMD 的实用性。
GMVMD 在模态对齐、信号减少误差等方面优于 MVMD。
GMVMD 可以比 MVMD 获得更准确的中心频率和更小的信号减少误差。
