Soler Andres, Muñoz-Gutiérrez Pablo A, Bueno-López Maximiliano, Giraldo Eduardo, Molinas Marta
Department of Engineering Cybernetics, Norwegian University of Science and Technology, Trondheim, Norway.
Department of Electronic Engineering, Universidad del Quindío, Armenia, Colombia.
Front Neurosci. 2020 Feb 28;14:175. doi: 10.3389/fnins.2020.00175. eCollection 2020.
Several approaches can be used to estimate neural activity. The main differences between them concern the information used and its sensitivity to high noise levels. Empirical mode decomposition (EMD) has been recently applied to electroencephalography EEG-based neural activity reconstruction to provide time-frequency information to improve the estimation of neural activity. EMD has the specific ability to identify independent oscillatory modes in non-stationary signals with multiple oscillatory components. However, attempts to use EMD in EEG analysis have not yet provided optimal reconstructions, due to the intrinsic mode-mixing problem of EMD. Several studies have used single-channel analysis, whereas others have used multiple-channel analysis for other applications. Here, we present the results of multiple-channel analysis using multivariate empirical mode decomposition (MEMD) to reduce the mode-mixing problem and provide useful time-frequency information for the reconstruction of neuronal activity using several low-density EEG electrode montages. The methods were evaluated using real and synthetic EEG data, in which the reconstructions were performed using the multiple sparse priors (MSP) algorithm with EEG electrode montages of 32, 16, and 8 electrodes. The quality of the source reconstruction was assessed using the Wasserstein metric. A comparison of the solutions without pre-processing and those after applying MEMD showed the source reconstructions to be improved using MEMD as information for the low-density montages of 8 and 16 electrodes. The mean source reconstruction error on a real EEG dataset was reduced by 59.42 and 66.04% for the 8 and 16 electrode montages, respectively, and that on a simulated EEG with three active sources, by 87.31 and 31.45% for the same electrode montages.
有几种方法可用于估计神经活动。它们之间的主要区别在于所使用的信息及其对高噪声水平的敏感性。经验模态分解（EMD）最近已应用于基于脑电图（EEG）的神经活动重建，以提供时频信息来改善神经活动的估计。EMD具有识别具有多个振荡成分的非平稳信号中独立振荡模式的特殊能力。然而，由于EMD固有的模式混叠问题，在EEG分析中使用EMD的尝试尚未提供最佳重建。一些研究使用单通道分析，而其他研究则在其他应用中使用多通道分析。在这里，我们展示了使用多变量经验模态分解（MEMD）进行多通道分析的结果，以减少模式混叠问题，并为使用几种低密度EEG电极蒙太奇重建神经元活动提供有用的时频信息。使用真实和合成EEG数据对这些方法进行了评估，其中使用具有32、16和8个电极的EEG电极蒙太奇，通过多重稀疏先验（MSP）算法进行重建。使用瓦瑟斯坦度量评估源重建的质量。对未进行预处理的解决方案与应用MEMD后的解决方案进行比较，结果表明，对于8电极和16电极的低密度蒙太奇，使用MEMD作为信息可改善源重建。在真实EEG数据集上，8电极和16电极蒙太奇的平均源重建误差分别降低了59.42%和66.04%，在具有三个活动源的模拟EEG上，相同电极蒙太奇的平均源重建误差分别降低了87.31%和31.45%。
