Cover
Vol. 16 No. 2 (2020)

Published: December 31, 2020

Pages: 81-90

Review Article

A Systematic Review of Brain-Computer Interface Based EEG

Samaa S. Abdulwahab 1 Hussain K. Khleaf Manal H. Jassim
1Electrical Engineering Department, University of Technology, Baghdad, Iraq
History
  • Received: October 7, 2020
  • Revised: October 18, 2020
  • Accepted: November 1, 2020
  • Available Online: November 1, 2020
DOI

https://doi.org/10.37917/ijeee.16.2.9

Abstract

The futuristic age requires progress in handwork or even sub-machine dependency and Brain-Computer Interface (BCI) provides the necessary BCI procession. As the article suggests, it is a pathway between the signals created by a human brain thinking and the computer, which can translate the signal transmitted into action. BCI-processed brain activity is typically measured using EEG. Throughout this article, further intend to provide an available and up-to-date review of EEG-based BCI, concentrating on its technical aspects. In specific, we present several essential neuroscience backgrounds that describe well how to build an EEG-based BCI, including evaluating which signal processing, software, and hardware techniques to use. Individuals discuss Brain-Computer Interface programs, demonstrate some existing device shortcomings, and propose some eld’s viewpoints.

Keywords

References

  1. P. Lahane, J. Jagtap, A. Inamdar, N. Karne, and R. Dev, “A review of recent trends in EEG based Brain-Computer Interface,” ICCIDS 2019 - 2nd Int. Conf. Comput. Intell. Data Sci. Proc., pp. 1–6, 2019, doi: 10.1109/ICCIDS.2019.8862054.
  2. J. Guan, “Advancements in the mind-machine interface: towards re-establishment of direct cortical control of limb movement in spinal cord injury,” ncbi.nlm.nih.gov, Accessed: Jul. 03, 2020. Abdulwahab, Khalef & Jasim https://www.ncbi.nlm.nih.gov/pmc/articles/PMC499443 8/.
  3. Nam, Chang S., Anton Nijholt, and Fabien Lotte, eds. Brain–computer interfaces handbook: technological and theoretical advances. CRC Press, 2018.
  4. J. R. Wolpaw et al., “Brain-computer interface technology: A review of the first international meeting,” IEEE Trans. Rehabil. Eng., vol. 8, no. 2, pp. 164–173, Jun. 2000, doi: 10.1109/TRE.2000.847807.
  5. J. J. Vidal, “Toward direct brain-computer communication.,” Annual review of biophysics and bioengineering, vol. 2. Annu Rev Biophys Bioeng, pp. 157–180, 1973, doi: 10.1146/annurev.bb.02.060173.001105.
  6. S. N. Abdulkader, A. Atia, and M. S. M. Mostafa, “Brain computer interfacing: Applications and challenges,” Egypt. Informatics J., vol. 16, no. 2, pp. 213– 230, Jul. 2015, doi: 10.1016/j.eij.2015.06.002.
  7. P. Z. Soroush and M. B. Shamsollahi, “A non-user- based BCI application for robot control,” 2018 IEEE EMBS Conf. Biomed. Eng. Sci. IECBES 2018 - Proc., pp. 36–41, 2019, doi: 10.1109/IECBES.2018.8626701.
  8. F. Gilbert, C. Pham, J. Viaña, and W. Gillam, “Increasing brain-computer interface media depictions: pressing ethical concerns,” Brain-Computer Interfaces, vol. 6, no. 3, pp. 49–70, 2019. doi: 10.1080/2326263X.2019.1655837.
  9. “Gartner Identifies Five Emerging Technology Trends That Will Blur the Lines Between Human and Machine.” https://www.gartner.com/en/newsroom/press- releases/2018-08-20-gartner-identifies-five-emerging- technology-trends-that-will-blur-the-lines-between- human-and-machine (accessed Sep. 26, 2020).
  10. J. G. Webster, F. Lotte, L. Bougrain, and M. Clerc, Electroencephalography (EEG)-Based Brain-Computer Interfaces, no. September. 2015.
  11. B. Allison, B. Graimann, and A. Gräser, “Why use a BCI if you are healthy,” BRAINPLAY - Brain-Computer Interfaces Games Work. ACE (Advances Comput. Entertain., pp. 1–5, 2007, Accessed: Aug. 03, 2020. http://hmi.ewi.utwente.nl/brainplay07_files/brainplay07 _proceedings.pdf#page=15.
  12. E. C. Lalor et al., “Steady-state VEP-based brain- computer interface control in an immersive 3D gaming environment,” EURASIP J. Appl. Signal Processing, vol. 2005, no. 19, pp. 3156–3164, Nov. 2005, doi: 10.1155/ASP.2005.3156.
  13. H. Nakasaki et al., “Mosaicism in the expression of tumor-associated carbohydrate antigens in human colonic and gastric cancers.,” Cancer Res., vol. 49, no. 13, pp. 3662–3669, Jul. 1989.
  14. U. R. Acharya et al., “Characterization of focal EEG signals: A review,” Futur. Gener. Comput. Syst., vol. 91, no. September, pp. 290–299, 2019, doi: 10.1016/j.future.2018.08.044.
  15. A. Bashashati, R. K. Ward, and G. E. Birch, “Towards Development of a 3-State Self-Paced Brain-Computer Interface,” Comput. Intell. Neurosci., vol. 2007, p. 84386, 2007, doi: 10.1155/2007/84386.
  16. R. Scherer et al., “The self-paced graz brain-computer interface: methods and applications,” Comput. Intell. Neurosci., vol. 2007, p. 79826, 2007, doi: 10.1155/2007/79826.
  17. X. Zhang, “A Survey on Deep Learning based Brain Computer Interface: Recent Advances and New Frontiers,” SIGIR 2019 - Proc. 42nd Int. ACM SIGIR Conf. Res. Dev. Inf. Retr., vol. 1, no. 1, pp. 1281–1284, 2019.
  18. M. Tariq, P. M. Trivailo, and M. Simic, “EEG-Based BCI Control Schemes for Lower-Limb Assistive- Robots,” Front. Hum. Neurosci., vol. 12, no. August, Aug. 2018, doi: 10.3389/fnhum.2018.00312.
  19. S. M. Nacy, S. N. Kbah, H. A. Jafer, I. Al-Shaalan, and I. A.-S. Somer M. Nacy*, Sadeem N. Kbah, Hind A. Jafer, “Controlling a Servo Motor Using EEG Signals from the Primary Motor Cortex,” Am. J. Biomed. Eng., vol. 6, no. 5, pp. 139–146, 2016, doi: 10.5923/j.ajbe.20160605.02.
  20. S. Al-Qaraawi, M. S. Croock, and S. H. Alawi, “Electroencephalography Signals Based Face Interaction for Directional Control System,” Int. J. Comput. Netw. Technol., vol. Volume 6, no. Issue 2, pp. 50–55, 2018, doi: 10.12785/ijcnt/060203.
  21. E. Yurci, “Emotion Detection from Eeg Signals: Correlating Cerebral Cortex Activity,” Dep. Inf. Commun. Technol. Pompeu Fabra, Barcelona, 2014.
  22. R. H. Abiyev, N. Akkaya, E. Aytac, I. Günsel, and A. Çaǧman, “Brain-Computer Interface for Control of Wheelchair Using Fuzzy Neural Networks,” Biomed Res. Int., vol. 2016, 2016, doi: 10.1155/2016/9359868.
  23. G. Schalk and J. Mellinger, A practical guide to brain- computer interfacing with BCI2000: General-purpose software for brain-computer interface research, data acquisition, stimulus presentation, and brain monitoring. Springer London, 2010.
  24. G. Schalk, D. J. McFarland, T. Hinterberger, N. Birbaumer, and J. R. Wolpaw, “BCI2000: A general- purpose brain-computer interface (BCI) system,” IEEE Trans. Biomed. Eng., vol. 51, no. 6, pp. 1034–1043, Jun. 2004, doi: 10.1109/TBME.2004.827072.
  25. “Welcome to the OpenBCI Community OpenBCI Documentation” https://docs.openbci.com/docs/Welcome (accessed Aug. 04, 2020).
  26. Duvinage, M., Castermans, T., Petieau, M. et al. Performance of the Emotiv Epoc headset for P300- based applications. BioMed Eng OnLine, Vol. 12, Issue 56, 2013. https://doi.org/10.1186/1475-925X-12-56.
  27. S. Narayana, R. R. V. Prasad, and K. Warmerdam, “Mind your thoughts: BCI using single EEG electrode,” IET Cyber-Physical Syst. Theory Appl., vol. 4, no. 2, pp. 164–172, 2019, doi: 10.1049/iet-cps.2018.5059.
  28. R. Maskeliunas, R. Damasevicius, I. Martisius, and M. Vasiljevas, “Consumer-grade EEG devices: Are they usable for control tasks?”, Peer J, vol. 2016, no. 3, pp. 1– 27, 2016, doi: 10.7717/peerj.1746.
  29. G. Thut and C. Miniussi, “New insights into rhythmic brain activity from TMS-EEG studies,” Trends in Abdulwahab, Khalef & Jasim | 89 Cognitive Sciences, vol. 13, no. 4. pp. 182–189, Apr. 2009, doi: 10.1016/j.tics.2009.01.004.
  30. R. A. Ramadan and A. V. Vasilakos, “Brain computer interface: control signals review,” Neurocomputing, vol. 223, no. 61272509, pp. 26–44, 2017, doi: 10.1016/j.neucom.2016.10.024.
  31. J. Clarke, “Principles and Applications of SQUIDs,” Proc. IEEE, vol. 77, no. 8, pp. 1208–1223, 1989, doi: 10.1109/5.34120.
  32. M. Hämäläinen, R. Hari, R. J. Ilmoniemi, J. Knuutila, and O. V. Lounasmaa, “Magnetoencephalography theory, instrumentation, and applications to noninvasive studies of the working human brain,” Rev. Mod. Phys., vol. 65, no. 2, pp. 413–497, Apr. 1993, doi: 10.1103/RevModPhys.65.413.
  33. J. Vrba, “Multichannel SQUID Biomagnetic Systems,” in Applications of Superconductivity, Springer Netherlands, 2000, pp. 61–138.
  34. M. Khalighi, B. Vosoughi Vahdat, M. Mortazavi, W. Hy, and M. Soleimani, “Practical design of low-cost instrumentation for industrial electrical impedance tomography (EIT),” in 2012 IEEE I2MTC - International Instrumentation and Measurement Technology Conference, Proceedings, 2012, pp. 1259–1263, doi: 10.1109/I2MTC.2012.6229173.
  35. Diez, Pablo, ed. Smart Wheelchairs and Brain- computer Interfaces: Mobile Assistive Technologies. Academic Press, 2018.
  36. Wolpaw, Jonathan, and Elizabeth Winter Wolpaw, eds. Brain-computer interfaces: principles and practice. OUP USA, 2012.
  37. V. Menon and S. Crottaz-Herbette, “Combined EEG and fMRI Studies of Human Brain Function,” Int. Rev. Neurobiol., vol. 66, no. 05, pp. 291–321, 2005, doi: 10.1016/S0074-7742(05)66010-2.
  38. M. O. Sokunbi, D. E. J. Linden, I. Habes, S. Johnston, and N. Ihssen, “Real-time fMRI brain-computer interface: Development of a ‘motivational feedback’ subsystem for the regulation of visual cue reactivity,” Front. Behav. Neurosci., vol. 8, no. NOV, Nov. 2014, doi: 10.3389/fnbeh.2014.00392.
  39. N. Naseer, “fNIRS-based brain-computer interfaces: a review,” frontiersin.org, Accessed: Aug. 06, 2020. https://www.frontiersin.org/articles/10.3389/fnhum.201 5.00003/full.
  40. M. Khan, “Passive BCI based on drowsiness detection: an fNIRS study,” osapublishing.org, Accessed: Aug. 06, 2020. https://www.osapublishing.org/abstract.cfm?uri=boe-6- 10-4063.
  41. K. S. Hong, M. J. Khan, and M. J. Hong, “Feature Extraction and Classification Methods for Hybrid fNIRS- EEG Brain-Computer Interfaces,” Front. Hum. Neurosci., vol. 12, no. June, pp. 1–25, 2018, doi: 10.3389/fnhum.2018.00246.
  42. M. J. Khan and K. S. Hong, “Hybrid EEG-FNIRS- based eight-command decoding for BCI: Application to quadcopter control,” Front. Neurorobot., vol. 11, no. FEB, Feb. 2017, doi: 10.3389/fnbot.2017.00006.
  43. S. Sen Purkayastha, V. K. Jain, and H. K. Sardana, “Topical Review: A Review of Various Techniques Used for Measuring Brain Activity in Brain Computer Interfaces,” Adv. Electron. Electr. Eng., vol. 4, no. 5, pp. 513–522, 2014.
  44. U. Fatema, “Motor Imagery Signal Classification using EEG and ECoG signal for Brain Computer Interface.” International IEEE/EMBS Conference on Neural Engineering, NER 2019
  45. M. Wang, R. Li, R. Zhang, G. Li, and D. Zhang, “A Wearable SSVEP-Based BCI System for Quadcopter Control Using Head-Mounted Device,” IEEE Access, 2018, doi: 10.1109/ACCESS.2018.2825378.
  46. C. M. Wong, B. Wang, Z. Wang, K. F. Lao, A. Rosa, and F. Wan, “Spatial Filtering in SSVEP-based BCIs: Unified Framework and New Improvements,” IEEE Trans. Biomed. Eng., no. March, pp. 1–1, 2020, doi: 10.1109/tbme.2020.2975552.
  47. X. Chen, B. Zhao, Y. Wang, S. Xu, and X. Gao, “Control of a 7-DOF Robotic Arm System with an SSVEP-Based BCI,” Int. J. Neural Syst., vol. 28, no. 8, pp. 1–15, Oct. 2018, doi: 10.1142/S0129065718500181.
  48. F. Velasco-Álvarez, S. Sancha-Ros, E. García-Garaluz, Á. Fernández-Rodríguez, M. T. Medina-Juliá, and R. Ron-Angevin, “UMA-BCI Speller: An easily configurable P300 speller tool for end users,” Comput. Methods Programs Biomed., vol. 172, pp. 127–138, Apr. 2019, doi: 10.1016/j.cmpb.2019.02.015.
  49. M. Li, W. Li, J. Zhao, Q. Meng, M. Zeng, and G. Chen, “A p300 model for cerebot - A mind-controlled humanoid robot,” in Advances in Intelligent Systems and Computing, 2014, vol. 274, pp. 495–502, doi: 10.1007/978-3-319-05582-4_43.
  50. K. Yoon and K. Kim, “Multiple kernel learning based on three discriminant features for a P300 speller BCI,” Neurocomputing, vol. 237, pp. 133–144, May 2017, doi: 10.1016/j.neucom.2016.09.053.
  51. I. Lazarou, S. Nikolopoulos, P. C. Petrantonakis, I. Kompatsiaris, and M. Tsolaki, “EEG-based brain– computer interfaces for communication and rehabilitation of people with motor impairment: A novel approach of the 21st century,” Front. Hum. Neurosci., vol. 12, no. January, pp. 1–18, 2018, doi: 10.3389/fnhum.2018.00014.
  52. K. W. Ha and J. W. Jeong, “Motor imagery EEG classification using capsule networks,” Sensors (Switzerland), vol. 19, no. 13, 2019, doi: 10.3390/s19132854.
  53. N. Padfield, J. Zabalza, H. Zhao, V. Masero, and J. Ren, “EEG-based brain-computer interfaces using motor- imagery: Techniques and challenges,” Sensors (Switzerland), vol. 19, no. 6, pp. 1–34, 2019, doi: 10.3390/s19061423.
  54. H. A. Shedeed, M. F. Issa, and S. M. El-Sayed, “Brain EEG signal processing for controlling a robotic arm,” Proc. - 2013 8th Int. Conf. Comput. Eng. Syst. ICCES 2013, pp. 152–157, 2013, doi: 10.1109/ICCES.2013.6707191.
  55. S. Sree Shankar and R. Rai, “Human factors study on the usage of BCI headset for 3D CAD modeling,” CAD Abdulwahab, Khalef & Jasim Comput. Aided Des., vol. 54, no. 1, pp. 51–55, 2014, doi: 10.1016/j.cad.2014.01.006.
  56. M. Ahn, M. Lee, J. Choi, and S. C. Jun, “A review of brain-computer interface games and an opinion survey from researchers, developers and users,” Sensors (Switzerland), vol. 14, no. 8, pp. 14601–14633, Aug. 2014, doi: 10.3390/s140814601.
  57. B. Kerous, F. Skola, and F. Liarokapis, “EEG-based BCI and video games: a progress report,” Virtual Real., vol. 22, no. 2, pp. 119–135, Jun. 2018, doi: 10.1007/s10055-017-0328-x.
  58. N. A. Badcock et al., “Validation of the Emotiv EPOC EEG systemfor research quality auditory event-related potentials in children,” PeerJ, vol. 2015, no. 3, 2015, doi: 10.7717/peerj.907.
  59. Y. Xia et al., “Tracking the dynamic functional connectivity structure of the human brain across the adult lifespan,” Hum. Brain Mapp., vol. 40, no. 3, pp. 717–728, Feb. 2019, doi: 10.1002/hbm.24385.
  60. C. Schmidt, D. Piper, B. Pester, A. Mierau, and H. Witte, “Tracking the reorganization of module structure in time-varying weighted brain functional connectivity networks,” Int. J. Neural Syst., vol. 28, no. 4, May 2018, doi: 10.1142/S0129065717500514.
  61. F. D. V. Fallani and D. S. Bassett, “Network neuroscience for optimizing brain-computer interfaces,” Phys. Life Rev., vol. 31, pp. 304–309, Jul. 2018, doi: 10.1016/j.plrev.2018.10.001.
  62. W. A. Jang, S. M. Lee, and D. H. Lee, “Development BCI for individuals with severely disability using EMOTIV EEG headset and robot,” 2014 International Winter Workshop on Brain-Computer Interface, BCI 2014 2014, doi: 10.1109/iww-BCI.2014.6782576.
  63. E. Maby, M. Perrin, O. Bertrand, G. Sanchez, and J. Mattout, “BCI could make old two-player games even more fun: A proof of concept with ‘connect Four,’” Adv. Human-Computer Interact., vol. 2012, p. 1, 2012, doi: 10.1155/2012/124728.
  64. L. D. J. Fiederer, M. Völker, R. T. Schirrmeister, W. Burgard, J. Boedecker, and T. Ball, “Hybrid brain- computer-interfacing for human-compliant robots: Inferring continuous subjective ratings with deep regression,” Front. Neurorobot., vol. 13, no. October, pp. 1–18, 2019, doi: 10.3389/fnbot.2019.00076.
  65. D. Das Chakladar and S. Chakraborty, “EEG based emotion classification using ‘correlation Based Subset Selection”, Biol. Inspired Cogn. Archit., vol. 24, pp. 98– 106, 2018, doi: 10.1016/j.bica.2018.04.012.
  66. G. Grübler et al., “Psychosocial and ethical aspects in non-invasive EEG-based BCI research - A survey among BCI users and BCI professionals,” Neuroethics, vol. 7, no. 1, pp. 29–41, 2014, doi: 10.1007/s12152-013-9179-7.
  67. F. E. Abd El-Samie, T. N. Alotaiby, M. I. Khalid, S. A. Alshebeili, and S. A. Aldosari, “A Review of EEG and MEG Epileptic Spike Detection Algorithms,” IEEE Access, vol. 6, no. October, pp. 60673–60688, 2018, doi: 10.1109/ACCESS.2018.2875487.
  68. P. Machado Vieira Lima, F. Ferrentini Sampaio, I. Bichara de Azeredo Coutinho, and G. Bonorino Xexéo, “Playing with robots using your brain Rubens Lacerda Queiroz,” pp. 548–555, 2018.
  69. C. Escolano, J. M. Antelis, and J. Minguez, “A telepresence mobile robot controlled with a noninvasive brain-computer interface,” IEEE Trans. Syst. Man, Cybern. Part B Cybern., vol. 42, no. 3, pp. 793–804, 2012, doi: 10.1109/TSMCB.2011.2177968.
  70. L. Bai, T. Yu, and Y. Li, “A brain computer interface- based explorer,” J. Neurosci. Methods, vol. 244, pp. 2–7, 2015, doi: 10.1016/j.jneumeth.2014.06.015.
  71. H. Göksu, “BCI oriented EEG analysis using log energy entropy of wavelet packets,” Biomed. Signal Process. Control, vol. 44, pp. 101–109, Jul. 2018, doi: 10.1016/j.bspc.2018.04.002.
  72. O. Landau, R. Puzis, and N. Nissim, “Mind your mind: EEG-based brain-computer interfaces and their security in cyber space,” ACM Comput. Surv., vol. 53, no. 1, 2020, doi: 10.1145/3372043.
  73. S. Hareendar, R. Jeya Raghul, and A. Kumaravelan, “Brain computer interface for controlling mobile robot,” Int. J. Recent Technol. Eng., vol. 8, no. 2 Special Issue 4, pp. 152–156, 2019, doi: 10.35940/ijrte.B1027.0782S419.
  74. M. Rashid et al., “Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review,” Front. Neurorobot., vol. 14, no. June, p. 25, Jun. 2020, doi: 10.3389/fnbot.2020.00025.
  75. H. J. Hwang, S. Kim, S. Choi, and C. H. Im, “EEG- based brain-computer interfaces: A thorough literature survey,” Int. J. Hum. Comput. Interact., vol. 29, no. 12, pp. 814–826, 2013. doi: 10.1080/10447318.2013.780869.
  76. S. Moghimi, A. Kushki, A. Marie Guerguerian, and T. Chau, “A review of EEG-Based brain-computer interfaces as access pathways for individuals with severe disabilities,” Assist. Technol., vol. 25, no. 2, pp. 99–110, Apr. 2013, doi: 10.1080/10400435.2012.723298.
  77. “(((Brain computer interface) OR (Bci)) OR (Brain machine interface)) OR (Bmi) -Search Results-PubMed.” https://pubmed.ncbi.nlm.nih.gov/?term=(((Brain computer interface) OR (Bci)) OR (Brain machine interface)) OR (Bmi)&timeline=expanded (accessed Aug. 31, 2020).
  78. Siuly, Siuly, Yan Li, and Yanchun Zhang. "EEG signal analysis and classification", IEEE Trans Neural Syst. Rehabilit Eng., vol. 11, pp. 141-4, 2016.
  79. A. Al-Saegh, “Comparison of Complex-Valued Independent Component Analysis Algorithms for EEG Data,” Iraqi J. Electr. Electron. Eng., vol. 15, no. 1, pp. 1–12, Jun. 2019, doi: 10.37917/ijeee.15.1.1.
  80. Z. Alhakeem and R. Ali, “Session to Session Transfer Learning Method Using Independent Component Analysis with Regularized Common Spatial Patterns for EEG-MI Signals,” Iraqi J. Electr. Electron. Eng., vol. 15, no. 1, pp. 13–27, Jun. 2019, doi: 10.37917/ijeee.15.1.2.