Averaging auditory evoked magnetoencephalographic and electroencephalographic responses: a critical discussion.
In the analysis of data from magnetoencephalography (MEG) and electroencephalography (EEG), it is common practice to arithmetically average event-related magnetic fields (ERFs) or event-related electric potentials (ERPs) across single trials and subsequently across subjects to obtain the so-called grand mean. Comparisons of grand means, e.g. between conditions, are then often performed by subtraction. These operations, and their statistical evaluation with parametric tests such as ANOVA, tacitly rely on the assumption that the data follow the additive model, have a normal distribution, and have a homogeneous variance. This may be true for single trials, but these conditions are rarely met when ERFs/ERPs are compared between subjects, meaning that the additive model is seldom the correct model for computing grand mean waveforms. Here, we summarize some of our recent work and present new evidence, from auditory-evoked MEG and EEG results, that the non-normal distributions and the heteroscedasticity observed instead result because ERFs/ERPs follow a mixed model with additive and multiplicative components. For peak amplitudes, such as the auditory M100 and N100, the multiplicative component dominates. These findings emphasize that the common practice of simply subtracting arithmetic means of auditory-evoked ERFs or ERPs is problematic without prior adequate transformation of the data. Application of the area sinus hyperbolicus (asinh) transform to data following the mixed model transforms them into the requested additive model with its normal distribution and homogeneous variance. We therefore advise checking the data for compliance with the additive model and using the asinh transform if required.
Special Laboratory for Non-invasive Brain Imaging, Leibniz Institute for Neurobiology, Brenneckestraße 6, 39118, Magdeburg, Germany.
Reinhard
R
König
Artur
A
Matysiak
Wojciech
W
Kordecki
Cezary
C
Sielużycki
Norman
N
Zacharias
Peter
P
Heil
D000465:Algorithms; D000818:Animals; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000379:methods*; D005072:Evoked Potentials, Auditory; D006801:Humans; D015225:Magnetoencephalography / Q000379:methods*
asinh transformation; grand mean; mixed model; variance stabilization