Document Detail


Time-dependent statistical and correlation properties of neural signals during handwriting.
MedLine Citation:
PMID:  22984455     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
To elucidate the cortical control of handwriting, we examined time-dependent statistical and correlational properties of simultaneously recorded 64-channel electroencephalograms (EEGs) and electromyograms (EMGs) of intrinsic hand muscles. We introduced a statistical method, which offered advantages compared to conventional coherence methods. In contrast to coherence methods, which operate in the frequency domain, our method enabled us to study the functional association between different neural regions in the time domain. In our experiments, subjects performed about 400 stereotypical trials during which they wrote a single character. These trials provided time-dependent EMG and EEG data capturing different handwriting epochs. The set of trials was treated as a statistical ensemble, and time-dependent correlation functions between neural signals were computed by averaging over that ensemble. We found that trial-to-trial variability of both the EMGs and EEGs was well described by a log-normal distribution with time-dependent parameters, which was clearly distinguished from the normal (Gaussian) distribution. We found strong and long-lasting EMG/EMG correlations, whereas EEG/EEG correlations, which were also quite strong, were short-lived with a characteristic correlation durations on the order of 100 ms or less. Our computations of correlation functions were restricted to the [Formula: see text] spectral range (13-30 Hz) of EEG signals where we found the strongest effects related to handwriting. Although, all subjects involved in our experiments were right-hand writers, we observed a clear symmetry between left and right motor areas: inter-channel correlations were strong if both channels were located over the left or right hemispheres, and 2-3 times weaker if the EEG channels were located over different hemispheres. Although we observed synchronized changes in the mean energies of EEG and EMG signals, we found that EEG/EMG correlations were much weaker than EEG/EEG and EMG/EMG correlations. The absence of strong correlations between EMG and EEG signals indicates that (i) a large fraction of the EEG signal includes electrical activity unrelated to low-level motor variability; (ii) neural processing of cortically-derived signals by spinal circuitry may reduce the correlation between EEG and EMG signals.
Authors:
Valery I Rupasov; Mikhail A Lebedev; Joseph S Erlichman; Stephen L Lee; James C Leiter; Michael Linderman
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.     Date:  2012-09-11
Journal Detail:
Title:  PloS one     Volume:  7     ISSN:  1932-6203     ISO Abbreviation:  PLoS ONE     Publication Date:  2012  
Date Detail:
Created Date:  2012-09-17     Completed Date:  2013-03-12     Revised Date:  2013-07-11    
Medline Journal Info:
Nlm Unique ID:  101285081     Medline TA:  PLoS One     Country:  United States    
Other Details:
Languages:  eng     Pagination:  e43945     Citation Subset:  IM    
Affiliation:
Department of Basic Research, Norconnect Inc, Ogdensburg, New York, United States of America.
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MeSH Terms
Descriptor/Qualifier:
Cerebral Cortex / physiology*
Color
Electrodes
Electroencephalography
Electromyography
Handwriting*
Humans
Neurons / physiology*
Probability
Signal Processing, Computer-Assisted
Statistics as Topic*
Surface Properties
Time Factors
Comments/Corrections

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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