Phase synchronization analysis of sensorimotor cortex electroencephalograms for channels c3 and C4

Rizwan Ur Rehman; Jahangir Muhammad; Sitwat Qureshi; Tanweer Iqbal; Imran Siddiqui; Syed Zeeshan Abbas

Author(s)

Rizwan Ur Rehman Departmnt of Physics, University of Karachi, Main University Rd, Karachi75270, Sindh Pakistan; Department of Physics, Govt. National College 1, Shaheed-e-Millat Rd., Karachi, Sindh Pakistan

Jahangir Muhammad Departmnt of Physics, University of Karachi, Main University Rd, Karachi75270, Sindh Pakistan; Department of Physics, Govt. National College 1, Shaheed-e-Millat Rd., Karachi, Sindh Pakistan

Sitwat Qureshi Department of Physics, Federal Urdu University of Arts, Science and Technology,University Rd, Block 9 Gulshane-Iqbal, Karachi, Sindh,Pakistan

Tanweer Iqbal Institute of Space and Planetary Astrophysics, University of Karachi,Main University Rd, Karachi-75270, Sindh,Pakistan

Imran Siddiqui Departmnt of Physics, University of Karachi,Main University Rd, Karachi75270, Sindh,Pakistan

Syed Zeeshan Abbas Departmnt of Physics, University of Karachi,Main University Rd, Karachi75270, Sindh,Pakistan

Abstract

Electroencephalograms (EEG) are electrical brain maps which arise as a result of neuronal activity occurring in the cortical region of human brain. These EEG signals on the scalp surface associated with cognition and motor response of brain and are a useful guide to study human behavior and its association with the underlying brain dynamics. Appearance of EEG activity as a result of some specific cognitive or motor event is an event-related activity and associated potential is known as Event-Related-Potential (ERP). Sensorimotor process related to motor activity or sensor recognition involving multiple cortical regions of the brain is the most critical and complex brain activity. To study such inter-related cortical excitations, we present event-related-synchronization (ERS) and event-related desynchronization (ERD) analysis of motor hand movement activity paradigm conducted 10 healthy human subjects. A predefined trial sequence is presented to the subjects and EEG signals are recorded on primary motor area C3, C4 are obtained in a non-invasive manner. Phase synchronization of recorded EEG signals at channels C3, C4 are determined. High phase coherence is observed for left hand movement on C3, C4 contralateral electrodes with a correlation coefficient of 0.6922 and for right hand movement correlation coefficient is 0.6561. EEG signals on two electrodes remain phase locked for duration of 300 - 500 ms. Spectral analysis reveals spectral band in alpha rhythm (8-13 Hz), for average values of frequency with 11.613 Hz for left-hand movement and 11.305 Hz for right-hand movement. EEG signals at other electrode positions show less or no phase synchronization for hand movement motor execution paradigm.

Publication Details

Page(s) 531-541

DOI DOI not available

Published Journal: International Journal of Biology and Biotechnology, Volume: 17, Issue: 3, Year: 2020

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Keywords

brain, EEG, BCI, NIM, synchronization

References

Almurshedi A.,A.K. Ismail A. .2014 .Cross coherence independent component analysis in resting and action states EEG discrimination. Journal of Physics: Conference Series, 546 : 1742.

Andrew M.,David H.,Daniel P.F.,Peter K. .2017 .EEG correlates of sensorimotor processing: Independent components involved in sensory and motor processing. Scientific Reports, 7(1) : 1-15.

Chaudhary U.,Birmbaumer N.,Murguialday A.R. .2016 .Brain-computer interfaces for communication and rehabilitation. Nature Reviews Neurology, 12(9) : 513-525.

Delorme A.,Makeig M.,Fabre-Thorpe M. .2002 .From Single Trials EEG to Brain Area Dynamics. , 44 : 1056-1064.

Guger C.,Harkam W.,Hertnaes C.,Pfurtscheller G. .1999 .Prosthetic Control by an EEG-based BrainComputer Interface. AAATE 5th European Conference for the Advancement of Assistive Technology, : 3-6.

Handayani N.,Haryanto F.,Khotimah S.N.,Arif I.,Taruno W.P. .2018 .Coherence and phase synchrony analyses of EEG signals in mild cognitive impairment(MCI). Polish Journal of Medical Physics and Engineering, 24(1) : 1-9.

Helsinki .2001 .. , 79(4) : .

Homan R.W.,Herman J.,Purdy P. .1987 .Cerebral location of international 10-20 system electrode placement. Electroencephalography and Clinical neurophysiology, 66(4) : 376-382.

Iriarte J.,Urrestarazu E.,Valencia M.,Alegre M.,Malanda A. .2003 .Independent Component Analysis as a Tool to Eliminate Artifacts in EEG: A quantitative study. Journal of clinical neurophysiology, 20(4) : 249-257.

Jahangir S.T.,Iqbal R.,Rizwan A.S.,Nasir S.M.,Naqvi S.M.,Siddiqui S.M. .2017 .Spectral and Spatial Feature Extraction of Electroencephalographic. Journal of Basic and Applied Sciences, 13 : 104-113.

Jerome N.S.,Donoghue J.P. .2000 .Plasticity and Primary Motor Cortex. Annual Review of Neuroscience, 23 : 393-415.

Banquet J.P. .1973 .Spectral Analysis of the EEG in meditation. Electroencephalography and Clinical Neurophysiology, 35 : 143-151.

Junfeng S.,Xiangfei H.,Shanbao T. .2012 .Phase Synchronization Analysis of EEG Signals. IEEE Transactions on Biomedical Engineering, 59(8) : 2254-2263.

Kroupi E.,Yazdani A.,Vesin J.M.,Ebrahimi T. .2013 .EEG Correlares of Pleasent and Unpleasent Odor Preception. ACM Transactions on Multimedia Computing Communications and Applications, 10(3) : .

Lebedev M.A.,Nicolelis M.A. .2017 .Brain-machine interfaces: From basic science to neuroprostheses and neurorehabilitation. Physiological Review, 97(2) : 767-837.

Akin M. .2002 .Comparison of Wavelet Transform and FFT Methods in the Analysis of EEG Signals. Journal of Medical Systems, 26(3) : 241-247.

McFarland D.J. .2008 .Brain-computer interface operation of robotic and prosthetic devices. Computer, 41 : 52-56.

Nunez P.L.,Srinivasan R.,Westdorp A.F.,Wijesinghe R.S.,Tucker D.M.,Silberstein R.B.,Cadusch P.J. .1997 .EEG coherency I: Statistics, reference electrode, volume conduction, Laplacians, cortical imaging, and interpretation at multiple scales. Electroencephalography and Clinical Neurophysiology, 103(5) : 499-515.

Quiroga R.Q.,Kraskov A.,Kreuz T.,Grassberger P. .2002 .Performance of different synchronization measures in real data: A case study on electroencephalographic signals. Physical Review E, 10(4) : .

Reed ,C.M.S S.J.,Nasuto S.J. .2007 .A novel approach to the detection of synchronisation in EEG based on empirical mode decomposition. Journal of Computational Neuroscience, 23(1) : 79-111.

Ronak B.,Sun Y.,Helian N.,Steffart T. .2018 .The Correlation between EEG Signals as Measured in Different Positions on Scalp Varying with Distance. Procedia Computer Science, 123 : 92-97.

Sankari Z.,Adeli Z. .2011 .Intrahemispheric, interhemispheric, and distal EEG coherence in Alzheimer's disease. Clinical Neurophysiology, 122(5) : 897-906.

Sivakami A,Devi S.S. .2015 .Analysis of EEG for motor imagery based classification of hand activities. International Journal of Biomedical Engineering and Science, 2(3) : 11-22.

Stoykov M.E. and S.,Madhavan M.E. and S. .2015 .Motor priming in neurorehabilitation. Journal of Neurologic Physical Therapy, 39(1) : 33-42.

Wang L.,Guo X.,Sun J.,Jin Z.,Tong S. .2012 .Cortical networks of hemianopia stroke patients: A graph theoretical analysis of EEG signals at resting state. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, : 49-52.

Wolpaw J.R.,Birbaumer N.,McFarland D.J .2002 .Brain-computer interfaces for communi cation and control. Clinical Neurophysiology, 113 : 767-791.

Yang L.,Mei L.L.,Ke L.L. .2016 .A multiwavelet-based time-varying model identification approach for timefrequency analysis of EEG signals. Neurocomputing, 193 : 106-114.

.2020 .. (Accepted for publication May, : .