International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

EEG and EMG based BCI System - A Review

Seeni Mohamed Aliar Maraikkayar S MDepartment of ECE, Sethu Institute of Technology, Virudhunagar, Tamilnadu, IndiaTamilselvi RDepartment of ECE, Sethu Institute of Technology, Virudhunagar, Tamilnadu, IndiaParisa beham MDepartment of ECE, Sethu Institute of Technology, Virudhunagar, Tamilnadu, IndiaSabah Afroze ADepartment of ECE, Sethu Institute of Technology, Virudhunagar, Tamilnadu, India

Vol 3 No 9 (2019): Volume 3, Issue 9, September 2019 | Pages: 17-22

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 05-09-2019

doi Logo

pdf iconFull Text PDF
Abstract

The analysis of Electromyography (EMG) signal is one of the effective determinants for the valuable prosthetic devices. Now a day various techniques have been proposed by the researchers for detecting the different hand movements and postures. Ideally, one would observe neural activity, extract signatures of movement intention, and use that as a trigger to provide assisted movement, the contingent feedback, and reward. This is the rationale behind any neuro-rehabilitation approach using restorative BCI (Brain Computer Interface). Recent researches combines Electroencephalogram (EEG) and EMG signals using the spectral power correlation (SPC) to create a hybrid BCI device for controlling a hand exoskeleton. This paper portrays the layout of various physiological signals and reviews the Electromyography (EMG) and Electroencephalography involvement in brain computer interfaces. This paper reviews various research challenges in EMG that booms in the medical era.

Keywords

EEG, EMG, Amputees, BCI and Rhythms


Citation of this Article

Seeni Mohamed Aliar Maraikkayar S M, Tamilselvi R, Parisa beham M, Sabah Afroze A, “EEG and EMG based BCI System - A Review” Published in International Research Journal of Innovations in Engineering and Technology (IRJIET), Volume 3, Issue 9, pp 17-22, September 2019.

Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
References
  1. José Rouillarda, Alban Duprèsa, François Cabestainga, Stéphanie Leclercqb et.al. “Hybrid BCI coupling EEG and EMG for severe motor disabilities”, Procedia Manufacturing 3, 29 – 36, 2015.
  2. Sivakumar Balasubramanian, Eliana Garcia-Cossio, Niels Birbaumer, Etienne Burdet, and Ander Ramos-Murgialday,“Is EMG a viable alternative to BCI for detecting movement intention in severe stroke”, IEEE Transactions on Biomedical Engineering, vol.65, No.12, December 2018.
  3. Andre Ferreira, Wanderley C Celeste, Fernando A Cheein, Teodiano F Bastos-Filho, Mario Sarcinelli-Filho, and Ricardo Carelli, “Human-machine interfaces based on EMG and EEG applied to robotic systems”, J Neuroeng Rehabil. Vol.5, no.10, 2008.
  4. Lin K , Cinetto A, Wang Y, Chen X, Gao S, Gao X., “An online hybrid BCI system based on SSVEP and EMG”, J Neural Eng, vol.13, no.2, 2016.
  5. Byung Chan Chang and Bo Hyeok Seo, “Development of New Brain Computer Interface based on EEG and EMG”, Proceedings of the 2008 IEEE International Conference on Robotics and Biomimetics, Bangkok, February 21 - 26, 2009.
  6. YoungJae Song and Francisco Sepulveda, “A Novel Technique for Selecting EMG-Contaminated EEG Channels in Self-Paced Brain-Computer Interface Task Onset”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, DOI 10.1109/TNSRE.2018.2847316.
  7. Artemiy Oleinikov, Berdakh Abibullaev, Almas Shintemirov, Michele Folgheraiter, “Feature Extraction and Real-Time Recognition of Hand Motion Intentions from EMGs via Artificial Neural Networks”, 6th International Conference on Brain-Computer Interface (BCI), 2018.
  8. Vibodha Yasas Sri Bandara and Asiri Nanayakkara, “Differentiation of signals generated by eye blinks and mouth clenching in a portable brain computer interface system”, ICIIS 2017.
  9. Debojyoti Seth, Debashis Chakraborty, Prasun Ghosal, Salil Kumar Sanyal, “Brain Computer Interfacing: A Spectrum Estimation Based Neurophysiological Signal Interpretation”, 4th International Conference on Signal Processing and Integrated Networks (SPIN), 2014.
  10. Zhaojie Ju, Gaoxiang Ouyang and Honghai Liu, “EMG-EMG Correlation Analysis for Human Hand Movements”, IEEE Workshop on Robotic Intelligence in Informationally Structured Space (RiiSS), 2013.
  11. Inchul Choi, Ilsun Rhiu, Yushin Lee, Myung Hwan Yun, Chang S. Nam, “A systematic review of hybrid brain-computer interfaces: Taxonomy and usability perspectives”, DOI: https://doi.org/10.1371/journal.pone.0176674, April 2017.
  12. Prasant Kumar Pattnaik and Jay Sarraf ,“Brain Computer Interface issues on hand movement”, Journal of King Saud University –Computer and Information Sciences, Vol.30, pp.18-24, 2018.
  13. Li Y.Q, Long J.Y, Wang C.C, Zhang H.H et.al., “ An EEG-based BCI system for 2-D cursor control by combining Mu/ Beta rhythm and P300 potential”,  IEEE Trans. Biomed. Eng, vol.57, pp. 2495–2505, 2010.
  14. Hortal E, Ianez E, Ubeda A, Perez-Vidal C, and Azorin J.M, “Combining a brain–machine interface and an electrooculography interface to perform pick and place tasks with a robotic arm”, Robot. Auton. Syst, vol.72, pp. 181–188, 2015.
  15. Buccino A.P, Keles H.O, and Omurtag A, “Hybrid EEG-fNIRS asynchronous brain–computer interface for multiple motor tasks”,  PLoS ONE, 2016.
  16. Zimmermann R, Marchal-Crespo L, Edelmann J, Lambercy O, Fluet M.C, Riener R et.al., “Detection of motor execution using a hybrid fNIRS-biosignal BCI: a feasibility study”,  J. Neuroeng. Rehabil, vol.10, 2013.
  17. Leeb R, Sagha H, Chavarriaga R, and Millan J.D, “A hybrid brain– computer interface based on the fusion of electroencephalographic and electromyographic activities” J. Neural Eng, vol.8, 2011.
  18. Lin K, Cinetto A, Wang Y.J, Chen X.G, Gao S.K, and Gao X.R, “An online hybrid BCI system based on SSVEP and EMG” , J. Neural Eng., vol.13,2016.
  19. Aziz F, Arof H, Mokhtar N, and Mubin M, “HMM based automated wheelchair navigation using EOG traces in EEG.”,  J. Neural Eng., vol.11, 2016.
  20. ShishkinS. L, Nuzhdin Y.O, Svirin E.P, Trofimov A.G, Fedorova A.A, Kozyrskiy B.L, et.al., “EEG negativity in fixations used for gaze-based control: toward converting intentions into actions with an eye–brain–computer interface”, Front. Neurosci, vol.10, 2016.
  21. Zhang L, He W, He C.H, and Wang P, “Improving mental task classification by adding high frequency band information”, J. Med. Syst, vol. 34, pp. 51–60, 2015.
  22. Wang H.T, Li Y.Q, Long J.Y, Yu T.Y, and Gu Z.H, “An asynchronous wheelchair control by hybrid EEG-EOG brain–computer interface”,  Cogn. Neurodynamics, vol. 8, pp.399–409, 2014.

Copyright @ 2026 IRJIET. All Right Reserved.

Licensed underCreative Commons Attribution 4.0 International License.