Abstract
The detection of event-related potentials (ERPs) through electroencephalogram (EEG) analysis is a well-established method for understanding brain functions during a cognitive process. To increase the signal-to-noise ratio (SNR) and stationarity of the data, ERPs are often filtered to a wideband frequency range, such as 0.05–30 Hz. Alternatively, a natural-filtering procedure can be performed through empirical mode decomposition (EMD), which yields intrinsic mode functions (IMFs) for each trial of the EEG data, followed by averaging over trials to generate the event-related modes. However, although the EMD-based filtering procedure has advantages such as a high SNR, suitable waveform shape, and high statistical power, one fundamental drawback of the procedure is that it requires the selection of an IMF (or a partial sum of a range of IMFs) to determine an ERP component effectively. Therefore, in this study, we propose an intrinsic ERP (iERP) method to overcome the drawbacks and retain the advantages of event-related mode analysis for investigating ERP components. The iERP method can reveal multiple ERP components at their characteristic time scales and suitably cluster statistical effects among modes by using a tailored definition of each mode’s neighbors. We validated the iERP method by using realistic EEG data sets acquired from a face perception task and visual working memory task. By using these two data sets, we demonstrated how to apply the iERP method to a cognitive task and incorporate existing cluster-based tests into iERP analysis. Moreover, iERP analysis revealed the statistical effects between (or among) experimental conditions more effectively than the conventional ERP method did.
Highlights
Introduction to empirical mode decomposition (EMD)The EMD method decomposes a series of signal into a generally small number of intrinsic mode functions (IMFs) through the sifting process
We propose the intrinsic Event-related potential (ERP) (iERP) analysis method and demonstrate its usage and advantages by using two examples
The complete ensemble EMD with adaptive n oise14–16 (CEEMDAN) algorithm used in this study is based on the 2014 improvement of the CEEMDAN algorithm proposed by Colominas et al.[14]
Summary
The iERP method integrates (1) an improved CEEMDAN method, (2) a complementary approach for white noise generation, (3) a method for organizing iERP modes, and (4) a method for clustering statistical effects. A topographical representation of the iERP method that involves clustering experimental effects in the 3D [channel, mode, time] domain through the standard CBnPP test is presented (the maximum distance between channels was set as 40 mm for identifying neighbors). In this compact form, the iERP effects in a specified electrode cluster are reorganized among the modes obtained from the aforementioned topographical analysis without the use of any further statistical tests. Topographical iERP analysis was performed according to the statistical contrast between the correctly detected left and right change trials within the 2D [channel, mode] iERP domain (one-sample CBnPP test ,the maximum distance between channels was set as 40 mm for identifying neighbors). This positive correlation indicated that the higher the participants’ VWM capacity, the more their hit-left versus hit-right contrast of iERPs was consistent with the expected antisymmetric topographical pattern (Fig. 5c, second graph)
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