May 18, 2021

Robust linear modelling in ElectroEncephaloGraphy can be obtained using single weights reflecting each single trials’ dynamics

<p>Being able to remove or weigh down the influence of outlier data is desirable for any statistical models. While Magnetic and ElectroEncephaloGraphic (MEEG) data used to average trials per condition, it is now becoming common practice to use information from all trials to build linear models. Individual trials can, however, have considerable weight and thus bias inferential results. Here, rather than looking for outliers independently at each data point, we apply the principal component projection (PCP) method at each channel, deriving a single weight per trial at each channel independently. Using both synthetic data and open EEG data, we show (1) that PCP is efficient at detecting a large variety of outlying trials; (2) how PCP derived weights can be implemented in the context of the general linear model with accurate control of type 1 family-wise error rate; and (3) that our PCP-based Weighted Least Square (WLS) approach leads to in increase in power at the group results comparable to a much slower Iterative Reweighted Least Squares (IRLS), although the weighting scheme is markedly different. Together, results show that WLS based on PCP weights derived upon whole trial profiles is an efficient method to weigh down the influence of outlier data in linear models.</p>
<p> bioRxiv Subject Collection: Neuroscience</p>
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