RESEARCH ON RECORDING AND FILTERING ELECTROMYOGRAM (EMG) SIGNALS

  • Robert Bela Nagy Doctoral Student at University of Oradea
  • Tiberiu Vesselenyi University of Oradea
  • Florin Popentiu Vladicescu University of Oradea, UNESCO Chairholder
Keywords: Electromyography, Electrooculography, EMG, EOG, eye muscle movements, mouse cursor control

Abstract

In this article will be presented a novel way to record and filter the Electromyogram (EMG) signal. EMG signals are generated when the muscles activates. In our case the user’s eye muscle movements in any direction will be recorded and filtered, so we will be able to observe when the user looks with his/her eyes up, down, left or right. In this article we use the non-invasive EMG signal recording system (which uses Ag/AgCl electrodes for recording) to differentiate the user’s eye positions. The EMG signals are recorded from near eyes positions, using only 3 differential channels out of 4 differential channels offered by a 24-bit Analog Digital Converter (ADC), model NI-9234 and it’s afferent NI-USB 9162 High Speed USB Carrier, both made by National Instruments (NI). The signal recording and filtering program is made in Matlab R2012b (mathworks.com).The experimental research presented in this article is made during the studies to realise the author’s doctoral thesis.

References

1. Špulák D., Čmejla R., Bačáková R., Kračmar B., Satrapová L., Novotný P., Muscle activity detection in electromyograms recorded during periodic movements, Computers in Biology and Medicine, Vol. 47, pp. 93-103, (2014).
2. Mesin L., Realtime identification of active regions in muscles from high density surface electromyogram, Computers in Biology and Medicine, Vol. 56, pp. 37-50, (2015).
3. Liu J., Ying D., Rymer W.Z., EMG burst presence probability: A joint time-frequency representation of muscle activity and its application to onset detection, Journal of Biomechanics, Vol. 48, pp. 1193-1197, (2015).
4. Yochum M., Binczak S., A wavelet based method for electrical stimulation artifacts removal in electromyogram, Biomedical Signal Processing and Control, Vol. 22, pp. 1-10, (2015).
5. Rodriguez-Falces J., Izquierdo M., Gonzalez-Izal M., Place N., Comparison of the power spectral changes of the voluntary surface electromyogram and M wave during intermittent maximal voluntary contractions, Eur J Appl Physiol, Vol. 114, pp. 1943-1954, (2014).
6. Arjunan S.P., Kumar D.K., Wheeler K., Shimada H., Spectral properties of surface electromyogram signal and change in muscle conduction velocity during isometric muscle contraction, SIViP, Vol. 9, pp. 261-266, (2015).
7. Andrzejewska R., Jaskólski A., Jaskólska A., Gobbo M., Orizio C., Electromyogram features during linear torque decrement and their changes with fatigue, Eur J Appl Physiol, Vol. 114, pp. 2105-2117, (2014).
8. Danko S.G., Gratcheva L.V., Boytsova J.A., Solovjeva M.L., Electromyogram of Pericranial Muscles in Frequency Bands β and γ Comparing Various Cognitive and Emotional States, Human Physiology, Vol. 40, pp. 117-124, (2014).
9. Lewis S., Russold M., Hahn M., Aszmann O.C., Fully Implantable Multichannel EMG Measurement System: First Results, Biosystems & Biorobotics, Vol. 7, pp. 51-59, (2014).
10. Johkura K., Kawabata Y., Amano Y., Kudo Y., Murata H., Kirimura S., Funabiki K., Bedside evaluation of smooth pursuit eye movements in acute sensory stroke patients, Journal of the Neurological Sciences, Vol. 348, pp. 269-271, (2015).
11. Christensen J.A.E., Zoetmulder M., Koch H., Frandsen R., Arvastson L., Christensen S.R., Jennum P., Sorensen H.B.D., Data-driven modeling of sleep EEG and EOG reveals characteristics indicative of pre-Parkinson’s and Parkinson’s disease, Journal of Neuroscience Methods, Vol. 235, pp. 262-276, (2014).
12. Chen L.L., Zhao Y., Zhang J., Zou J.Z., Automatic detection of alertness/drowsiness from physiological signals using wavelet-based nonlinear features and machine learning, Expert Systems with Applications, Vol. 42, pp. 7344-7355, (2015).
13. Hortal E., Iáñez E., Úbeda A., Perez-Vidal C., Azorín J.M., Combining a Brain-Machine Interface and an Electrooculography Interface to perform pick and place tasks with a robotic arm, Robotics and Autonomous Systems, Vol 72., pp. 181-188, (2015).
14. Belkacem A.N., Shin D., Kambara H., Yoshimura N., Koike Y., Online classification algorithm for eye-movement-based communication systems using two temporal EEG sensors, Biomedical Signal Processing and Control, Vol. 16, pp. 40-47, (2015).
15. Pal A., Gautam A.K., Singh Y.N., Evaluation of Bioelectric Signals for Human Recognition, Procedia Computer Science, Vol. 48, pp. 746-752, (2015).
16. Wang C., Lu W., Narayanan M.R., Redmond S.J., Lovell N.H., Low-power Technologies for Wearable Telecare and Telehealth Systems: A Review, Biomed Eng Lett, Vol. 5, pp. 1-9, (2015).
17. Zhang J., Wang B., Hong J., Li T., Guo F., Human Manipulator Shared Online Control Using Electrooculography, ICIRA, Part I., LNAI 8917, pp. 278-287, (2014).
18. N.I. 9234 ADC Converter product page: http://sine.ni.com/nips/cds/view/p/lang/en/nid/208802
19. N.I. USB-9162 Carrier product page: http://sine.ni.com/nips/cds/view/p/lang/en/nid/204178
Published
2015-09-30
How to Cite
Nagy, R., Vesselenyi, T., & Vladicescu, F. (2015). RESEARCH ON RECORDING AND FILTERING ELECTROMYOGRAM (EMG) SIGNALS. Nonconventional Technologies Review, 19(3). Retrieved from http://revtn.ro/index.php/revtn/article/view/144