Key result
Multivariate Empirical Mode Decomposition cuts low-density EEG source reconstruction error by ~60%.
Why the study?
Attempts to use empirical mode decomposition in EEG analysis have not provided optimal reconstructions due to intrinsic mode-mixing.
Does multivariate empirical mode decomposition (MEMD) improve neural activity reconstruction using low-density EEG montages compared to no pre-processing?
Does multivariate empirical mode decomposition (MEMD) improve neural activity reconstruction using low-density EEG montages compared to no pre-processing?
Effect estimate: reduced by 59.42% (8 electrodes) and 66.04% (16 electrodes)
MEMD pre-processing significantly improves the accuracy of neural activity source reconstruction when using low-density EEG montages.
May aid low-density EEG source reconstruction; leaves open clinical validation and outcome impact.
Several approaches can be used to estimate neural activity. The main differences between them concern the a priori 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 a priori 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 a priori 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 a priori 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.
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Soler et al. (2020) studied Healthy subjects (face perception paradigm) (n=16). Multivariate Empirical Mode Decomposition (MEMD) pre-processing vs. Raw EEG data without pre-processing was evaluated on Mean source reconstruction error (Wasserstein metric) (reduced by 59.42% (8 electrodes) and 66.04% (16 electrodes)). Pre-processing low-density EEG data with Multivariate Empirical Mode Decomposition reduced the mean source reconstruction error by 59.42% and 66.04% for 8 and 16 electrode montages, respectively.
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