Mathematics  Neuroscience

Neuronal Variability during Handwriting: Lognormal Distribution
Published:
Friday, April 13, 2012
Author:
Valery I. Rupasov et al.
by Valery I. Rupasov, Mikhail A. Lebedev, Joseph S. Erlichman, Michael Linderman
We examined timedependent statistical properties of electromyographic (EMG) signals recorded from intrinsic hand muscles during handwriting. Our analysis showed that trialtotrial neuronal variability of EMG signals is well described by the lognormal distribution clearly distinguished from the Gaussian (normal) distribution. This finding indicates that EMG formation cannot be described by a conventional model where the signal is normally distributed because it is composed by summation of many random sources. We found that the variability of temporal parameters of handwriting  handwriting duration and response time  is also well described by a lognormal distribution. Although, the exact mechanism of lognormal statistics remains an open question, the results obtained should significantly impact experimental research, theoretical modeling and bioengineering applications of motor networks. In particular, our results suggest that accounting for lognormal distribution of EMGs can improve biomimetic systems that strive to reproduce EMG signals in artificial actuators.
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