Time-Dependent Statistical and Correlation Properties of Neural Signals during Handwriting

Valery I Rupasov,Stephen L Lee,Mikhail A Lebedev,Joseph S Erlichman,Michael Linderman,James C Leiter,Natasha M Maurits

doi:10.1371/journal.pone.0043945

Valery I Rupasov, Stephen L Lee + Show 5 more

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https://doi.org/10.1371/journal.pone.0043945

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Journal: PloS one	Publication Date: Sep 11, 2012
Citations: 13	License type: CC BY 4.0

Affiliation: Duke University, St. Lawrence University

Abstract

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 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.

Highlights

As in the case of EMG signals recorded from muscle groups involved in handwriting [13], we found that trial-to-trial variability of the ‘‘energy’’ of the EEGs recorded from the motor cortex area had a log-normal distribution, which was clearly distinguishable from the normal (Gaussian) distribution
To derive a dynamical picture of neural activity and of functional relationships between different neural regions, we examined time-dependent statistical and correlation properties of EMG and EEG signals recorded simultaneously during handwriting of digit ‘‘3’’ by 7 subjects
To study the time dependence of neural signals, the trials were divided into 20 100-ms time intervals

Summary

Introduction

Walter [1], the coherence method, developed for the analysis of stationary random data in linear systems (see, e.g., [2]), has been employed in hundreds of papers dealing with the analysis of neural signals such as EEGs and EMGs. Walter [1], the coherence method, developed for the analysis of stationary random data in linear systems (see, e.g., [2]), has been employed in hundreds of papers dealing with the analysis of neural signals such as EEGs and EMGs In these publications, the level of coherence was used as a measure of coupling between the processes generating neural signals and of the functional association between neuronal structures [3,4,5,6]. The level of coherence was used as a measure of coupling between the processes generating neural signals and of the functional association between neuronal structures [3,4,5,6] This analysis of relationships between neural signals is based on computations of the coherence and phase of the two signals. For Fourier harmonics, X (v) and Y (v), of two time-dependent signals X (t) and Y (t), the coherence is defined as the square of the modulus, C(v)~DP(v)D2, and the phase is defined as W(v)~arctan1⁄2Im P(v)=Re P(v), of the complex coherence function X (v)Y Ã(v)

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