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ORIGINAL ARTICLE
Year : 2021  |  Volume : 11  |  Issue : 4  |  Page : 229-236

The relation between chaotic feature of surface eeg and muscle force: Case study report


1 Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 Department of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, Iran

Correspondence Address:
Parisa Rangraz
Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehra
Iran
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jmss.JMSS_47_20

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Background: Nonlinear dynamics, especially the chaos characteristics, are useful in analyzing bio-potentials with many complexities. In this study, the evaluation of arm-tip force estimation method from the electroencephalography (EEG) signal in the vertical plane has been studied and chaos characteristics, including fractal dimension, Lyapunov exponent, entropy, and correlation dimension characteristics of EEG signals have been measured and analyzed at different levels of forces. Method: Electromyography signal was recorded with the help of the BIOPEC device (the Mp-100 model) and from the forearm muscle with surface electrodes, and the EEG signals were recorded from five major motor-related cortical areas according to 10–20 standard three times in a normal healthy 33-year-old male, athlete and right handed simultaneously with importing a force to 10 sinkers weighing from 10 to 100 Newton with step 10 Newton. Results: The findings confirm that force estimation through EEG signals is feasible, especially using fractal dimension feature. The R-squared values for Fractal dimension, Lyapunov exponent, and entropy and correlation dimension features for linear trend line were 0.93, 0.7, 0.86, and 0.41, respectively. Conclusion: The linear increase of characteristics especially fractal dimension and entropy, together with the results from other EEG and neuroimaging studies, suggests that under normal conditions, brain recruits motor neurons at a linear progress when increasing the force.


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