Information Transfer Index-A Promising Measure of the Corticomusclar Interaction


It is generally believed that a major cause of motor dysfunction is the impairment in neural network that controls movement. But little is known about the underlying mechanisms of the impairment in cortical control or in the neural connections between cortex and muscle that lead to the loss of motor ability. So understanding the functional connection between motor cortex and effector muscle is of utmost importance. Previous study mostly relied on cross-correlation, coherence functions or model based approaches such as Granger causality or dynamic causal modeling. In this work the information transfer index (ITI) was introduced to describe the information flows between motor cortex and muscle. Based on the information entropy the ITI can detect both linear and nonlinear interaction between two signals and thus represent a very comprehensive way to define the causality strength. The applicability of ITI is investigated based on simulations and electroencephalogram (EEG), surface electromyography (sEMG) recordings in a simple motor task.

Share and Cite:

Xie, P. , Ma, P. , Chen, X. , Li, X. and Su, Y. (2013) Information Transfer Index-A Promising Measure of the Corticomusclar Interaction. Engineering, 5, 57-61. doi: 10.4236/eng.2013.510B012.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] B. A. Conway, D. M. Halliday and U. Shahani, “Common Frequency Components Identified from Correlations between Magnetic Recordings of Cortical Activity and the Electromyogram in Man,” Journal of Physiology, Vol. 483, 1995, pp. 35-69.
[2] M.A. Perez, D. S. Soteropoulos and S. N. Baker, “Corticomuscular Coherence during Bilateral Isometric Arm Voluntary Activity in Healthy Humans,” Journal of Neurophysiology, Vol. 107, 2012, pp. 2154-2162.
[3] V. Chakarov, J. Rau, Naranjo and J. Schulte-Monting, “Beta-Range EEG-EMG Coherence with Isometric Compensation for Increasing Modulated Low-Level Forces,” Journal of Neurophysiology, Vol. 102, 2009, pp. 1115-1120.
[4] Y. Fang, J. J. Daly and J. Sun, “Functional Corticomuscular Connection during Reaching Is Weakened Following Stroke,” Clinical Neurophysiology, Vol. 120, 2009, pp. 994-1002.
[5] F. Vecchio and C. Babiloni, “Direction of Information Flow in Alzheimer’s Disease and MCI Patients,” International Journal of Alzheimer’s Disease, Vol. 10, 2011, pp. 1168-1179.
[6] C. L. Witham, C. N. Riddle and M. R. Bakerand, “Contributions of Descending and Ascending Pathways to Corticomuscular Coherence in Humans,” The Journal of Physiology, Vol. 589, 2011, pp. 3789-3800.
[7] S.-H. Jin, P. Lin and M. Hallett, “Linear and Nonlinear Information Flow Based on Time Delayed Mutual Information Method and Its Application to Corticomuscular Interaction,” Clinical Neurophysiology, Vol. 121, 2010, pp. 392-418.
[8] T. Schreiber, “Measuring Information Transfer,” Physical Review Letters, Vol. 85, 2000, pp. 461-464.
[9] B. Gourevitch and J. J. Eggermont, “Evaluating Informa- tion Transfer between Auditory Cortical Neurons,” Journal of Neurophysiology, Vol. 97, 2007, pp. 533-2543.
[10] R. Vicente, M. Wibral, M. Lindner and G. Pipa, “Transfer Entropy—A Model-Free Measure of Effective Connectivity for the Neurosciences,” Journal of Computational Neuroscience, Vol. 30, 2011, pp. 45-67.
[11] P. Xie, Y. H. Du and S. F. Huang, “Study of Feature Extraction Method for Complex Mechanical System Based on Information Entropy,” Journal of Viberation and Shock, Vol. 27, 2007, pp. 135-128.
[12] L. Cao, “Practical Method for Determining the Minimum Embedding Dimension of a Scalar Time Series,” Physica, Vol. 110, 1997, pp. 43-50.

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.