Advertisement

Health and Technology

, Volume 9, Issue 5, pp 765–789 | Cite as

Bio-potentials for smart control applications

  • Ajit Madhukerrao ChoudhariEmail author
  • Venkatesh Jonnalagedda
Original Paper
  • 83 Downloads

Abstract

This study demonstrates the capabilities of bio-potentials especially Electrooculography (EOG) for smart environmental control applications. Furthermore, In this study, we have proposed a novel, user-friendly, low cost, and wearable Man Machine Interface (MMI) for practical use out of a controlled laboratory environment. EOG based MMI framework is designed by using consumer-grade single channel Brain-Computer Interface (BCI) device. Acquired signals are filtered and processed for feature extraction. Eye blink signals are detected and decoded using Application Program Interface (API) developed in MATLAB. Four setups for real-time control experiments spanning over 300 trials are conducted to test the efficiency of EOG signals for smart environmental control applications. Additionally, real-time wheelchair control experiments are performed by five volunteers for testing and cross-validating the quantitative and qualitative factors of designing MMI. Finally, results of wheelchair control experiments are compared and contrasted with similar established MMI frameworks. Overall, Four setups of smart control experiments conveyed an average precision of 96.44%, 99.30%, 97.11%, and 95.78%, respectively, with a good response time ranging between 1.65 s to 2.23 s for two participants with 300 trials. Whereas for wheelchair control experiment, wherein five subjects volunteered, an average accuracy of 93.89% and 62.29 bits/min of Information Transfer Rate (ITR) were obtained with zero collision and zero False Positive Rate (FPR). From the results it can be inferred that, EOG signals can be applied to different smart environment control application with great potential; Proposed single channel EOG based MMI framework can provide a robust, practical, user-friendly yet at the same time precise and responsive interface for control applications at a moderate cost. In this investigation, an effective, low cost, wearable inter has been presented for the variety of users and various applications. When contrasted with the recently established comparative MMIs, the displayed framework gives a novel and proficient interfaces without Graphical User Interface (GUI) with more prominent exactness and better ITR.

Keywords

Electrooculography Biopotentials Wearable HMI Assistive technology Smart control Single channel MMI Capability testing 

Notes

Funding

This study did not receive any kind of funding or research grant from any institution/organization/individual.

Compliance with Ethical Standards

Conflict of interests

Authors Ajit Madhukerrao Choudhari and Venkatesh Jonnalagedda declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with animals performed by any of the authors. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

References

  1. 1.
    Curran E. Learning to control brain activity: a review of the production and control of EEG components for driving brain–computer interface (BCI) systems. Brain and Cogn 2003;51(3):326–336.  https://doi.org/10.1016/s0278-2626(03)00036-8.CrossRefGoogle Scholar
  2. 2.
    Nicolelis MAL. Actions from thoughts. Nature 2001;409(6818):403–407.  https://doi.org/10.1038/35053191.CrossRefGoogle Scholar
  3. 3.
    Vaughan T. Guest editorial brain-computer interface technology: a review of the second international meeting. IEEE Trans Neural Syst Rehabil Eng 2003;11(2):94–109.  https://doi.org/10.1109/tnsre.2003.814799.CrossRefGoogle Scholar
  4. 4.
    Xu Q, Zhou H, Wang Y, Huang J. Fuzzy support vector machine for classification of EEG signals using wavelet-based features. Med Eng Phys 2009;31(7):858–865.  https://doi.org/10.1016/j.medengphy.2009.04.005.CrossRefGoogle Scholar
  5. 5.
    Yuan H, He B. Brain computer interfaces using sensorimotor rhythms: Current state and future perspectives,. IEEE Trans Biomed Eng 2014;61(5):1425–1435.  https://doi.org/10.1109/tbme.2014.2312397.CrossRefGoogle Scholar
  6. 6.
    Farina D, Jensen W , Akay M, (eds). 2013. Introduction to neural engineering for motor rehabilitation. New York: Wiley.  https://doi.org/10.1002/9781118628522.Google Scholar
  7. 7.
    Punsawad Y, Wongsawat Y, Parnichkun M. Hybrid EEG-EOG brain-computer interface system for practical machine control. 2010 annual international conference of the IEEE engineering in medicine and biology. IEEE; 2010.Google Scholar
  8. 8.
    Barea R, Boquete L, Bergasa LM, López E , Mazo M. Electro-oculographic guidance of a wheelchair using eye movements codification. Int J Robot Res 2003;22(7–8):641–652.  https://doi.org/10.1177/02783649030227012.CrossRefGoogle Scholar
  9. 9.
    Aungsakun S, Phinyomark A, Phukpattaranont P, Limsakul C. Robust eye movement recognition using EOG signal for human-computer interface. Berlin: Springer; 2011. p. 714–723.  https://doi.org/10.1007/978-3-642-22191-0_63.CrossRefGoogle Scholar
  10. 10.
    Barea R, Boquete L, Mazo M, Lopez E. System for assisted mobility using eye movements based on electrooculography. IEEE Trans Neural Syst Rehabil Eng 2002;10(4):209–218.CrossRefGoogle Scholar
  11. 11.
    Wu JF, Ang AMS, Tsui KM, Wu HC, Hung YS, Hu Y, Mak JNF, Chan SC, Zhang Z. Efficient implementation and design of a new single-channel electrooculography-based human-machine interface system. IEEE Trans Circuits Syst Express Briefs 2015;62(2):179–183.  https://doi.org/10.1109/tcsii.2014.2368617.CrossRefGoogle Scholar
  12. 12.
    Farwell L, Donchin E. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr Clin Neurophysiol 1988;70(6):510–523.CrossRefGoogle Scholar
  13. 13.
    Guger C, Daban S, Sellers E, Holzner C, Krausz G, Carabalona R, Gramatica F, Edlinger G. How many people are able to control a p300-based brain-computer interface (BCI)? Neurosci Lett 2009;462(1): 94–98.  https://doi.org/10.1016/j.neulet.2009.06.045.CrossRefGoogle Scholar
  14. 14.
    Kaufmann T, Schulz SM, Grünzinger C, Kübler A. Flashing characters with famous faces improves ERP-based brain-computer interface performance. J Neural Eng 2011;8(5):056016.  https://doi.org/10.1088/1741-2560/8/5/056016  https://doi.org/10.1088/1741-2560/8/5/056016.CrossRefGoogle Scholar
  15. 15.
    Long J, Li Y, Wang H, Yu T, Pan J, Li F. A hybrid brain computer interface to control the direction and speed of a simulated or real wheelchair. IEEE Trans Neural Syst Rehabil Eng 2012;20(5):720–729.CrossRefGoogle Scholar
  16. 16.
    Pfurtscheller G, da Silva FL. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol 1999;110(11):1842–1857.CrossRefGoogle Scholar
  17. 17.
    Bin G, Gao X, Yan Z, Hong B, Gao S. An online multi-channel SSVEP-based brain–computer interface using a canonical correlation analysis method. J Neural Eng 2009;6(4):046002.CrossRefGoogle Scholar
  18. 18.
    Middendorf M, McMillan G, Calhoun G, Jones K. Brain-computer interfaces based on the steady-state visual-evoked response. IEEE Trans Rehabil Eng 2000;8(2):211–214.CrossRefGoogle Scholar
  19. 19.
    Birbaumer N, Ghanayim N, Hinterberger T, Iversen I, Kotchoubey B, Kübler A., Perelmouter J, Taub E, Flor H. A spelling device for the paralysed. Nature 1999;398(6725):297–298.CrossRefGoogle Scholar
  20. 20.
    Doud AJ, Lucas JP, Pisansky MT, He B. Continuous three-dimensional control of a virtual helicopter using a motor imagery based brain-computer interface. PLoS One 2011;6(10):e26322.CrossRefGoogle Scholar
  21. 21.
    Duan F, Lin D, Li W, Zhang Z. Design of a multimodal EEG-based hybrid BCI system with visual servo module. IEEE Trans Auton Ment Dev 2015;7(4):332–341.CrossRefGoogle Scholar
  22. 22.
    Koo B, Nam Y, Choi S. A hybrid EOG-p300 BCI with dual monitors. 2014 international winter workshop on brain-computer interface (BCI). IEEE; 2014.  https://doi.org/10.1109/iww-bci.2014.6782566.
  23. 23.
    Ma J, Zhang Y, Cichocki A, Matsuno F. A novel EOG/EEG hybrid human machine interface adopting eye movements and erps: Application to robot control. IEEE Trans Biomed Eng 2015;62(3):876–889.CrossRefGoogle Scholar
  24. 24.
    Royer AS, Doud AJ, Rose ML, He B. EEG control of a virtual helicopter in 3-dimensional space using intelligent control strategies. IEEE Trans. Neural Syst Rehabil Eng 2010;18(6):581–589.CrossRefGoogle Scholar
  25. 25.
    Yong X, Fatourechi M, Ward RK, Birch GE. The design of a point-and-click system by integrating a self-paced brain-computer interface with an eye-tracker. IEEE J Emerging Sel Top Circuits Syst 2011;1(4):590–602.  https://doi.org/10.1109/jetcas.2011.2175589.CrossRefGoogle Scholar
  26. 26.
    Galán F., Nuttin M, Lew E, Ferrez P, Vanacker G, Philips J, del Millán JR. A brainactuated wheelchair: Asynchronous and non-invasive brain-computer interfaces for continuous control of robots,. Clin Neurophysiol 2008;119(9):2159–2169.CrossRefGoogle Scholar
  27. 27.
    Grewal H, Matthews A, Tea R, George K. LIDAR-based autonomous wheelchair. IEEE sensors applications symposium (SAS). IEEE; 2017.  https://doi.org/10.1109/sas.2017.7894082.
  28. 28.
    Li Z, Lei S, Su C-Y, Li G. Hybrid brain/muscle-actuated control of an intelligent wheelchair. International conference on robotics and biomimetics (ROBIO). IEEE; 2013.  https://doi.org/10.1109/robio.2013.6739429.
  29. 29.
    Rebsamen B, Guan C, Zhang H, Wang C, Teo C, Ang MH, Burdet E. A brain controlled wheelchair to navigate in familiar environments. IEEE Trans Neural Syst Rehabil Eng 2010;18(6):590–598.CrossRefGoogle Scholar
  30. 30.
    Yu Y, Zhou Z, Yin E, Jiang J, Tang J, Liu Y, Hu D. Toward brain-actuated car applications: Self-paced control with a motor imagery-based brain-computer interface. Comput Biol Med 2016;77:148–155.CrossRefGoogle Scholar
  31. 31.
    Xu F, Zhou W, Zhen Y, Yuan Q. Classification of motor imagery tasks for electrocorticogram based brain-computer interface. Biomed Eng Lett 2014;4(2):149–157.  https://doi.org/10.1007/s13534-014-0128-0.CrossRefGoogle Scholar
  32. 32.
    Bastos-Filho TF, Cheein FA, Muller SMT, Celeste WC, de la Cruz C, Cavalieri DC, Sarcinelli-Filho M, Amaral PFS, Perez E, Soria CM, Carelli R. Towards a new modality-independent interface for a robotic wheelchair. IEEE Trans Neural Syst Rehabil Eng 2014;22(3):567–584.  https://doi.org/10.1109/tnsre.2013.2265237.CrossRefGoogle Scholar
  33. 33.
    Al-Haddad A, Sudirman R, Omar C, Hui KY, Jimin MR. Wheelchair motion control guide using eye gaze and blinks based on PointBug algorithm. 2012 third international conference on intelligent systems modelling and simulation. IEEE; 2012.  https://doi.org/10.1109/isms.2012.23.
  34. 34.
    Nakanishi M, Mitsukura Y. Wheelchair control system by using electrooculogram signal processing. The 19th Korea-Japan joint workshop on frontiers of computer vision. IEEE; 2013.  https://doi.org/10.1109/fcv.2013.6485476.
  35. 35.
    Duguleana M, Mogan G. Using eye blinking for EOG-based robot control. IFIP advances in information and communication technology. Berlin: Springer; 2010. p. 343–350.  https://doi.org/10.1007/978-3-642-11628-5_37.Google Scholar
  36. 36.
    Shen H-M, Hu L, Lee KM, Fu X. Multi-motion robots control based on bioelectric signals from single-channel dry electrode. Proc Inst Mech Eng H J Eng Med 2015;229(2):124–136.CrossRefGoogle Scholar
  37. 37.
    Deng LY, Hsu C-L, Lin T-C, Tuan J-S, Chang S-M. EOG-Based human-computer interface system development. Expert Syst Appl 2010;37(4):3337–3343.CrossRefGoogle Scholar
  38. 38.
    El-Halabi M, Haidar R, Kadri RE, Lahoud C. Eye-blinks communication vehicle: a prototype. 2017 fourth international conference on advances in biomedical engineering (ICABME). IEEE; 2017.  https://doi.org/10.1109/icabme.2017.8167567.
  39. 39.
    Borghetti D, Bruni A, Fabbrini M, Murri L, Sartucci F. A low-cost interface for control of computer functions by means of eye movements. Comput Biol Med 2007;37(12):1765–1770.CrossRefGoogle Scholar
  40. 40.
    Królak A, Strumiłło P. Eye-blink detection system for human-computer interaction. Univ Access Inf Soc 2011;11(4):409–419.  https://doi.org/10.1007/s10209-011-025.CrossRefGoogle Scholar
  41. 41.
    Usakli AB, Gurkan S, Aloise F, Vecchiato G, Babiloni F. On the use of electrooculogram for efficient human computer interfaces. Comput Intell Neurosci 2010;2010:1–5.  https://doi.org/10.1155/2010/135629.CrossRefGoogle Scholar
  42. 42.
    Yamagishi K, Hori J, Miyakawa M. Development of EOG-based communication system controlled by eight-directional eye movements. 2006 international conference of the IEEE engineering in medicine and biology society. IEEE; 2006,  https://doi.org/10.1109/iembs.2006.259914.
  43. 43.
    Aungsakul S, Phinyomark A, Phukpattaranont P, Limsakul C. Evaluating feature extraction methods of electrooculography (EOG) signal for human-computer interface. Procedia Engineering 2012;32:246–252.  https://doi.org/10.1016/j.proeng.2012.01.1264.CrossRefGoogle Scholar
  44. 44.
    Barea R, Boquete L, Ortega S, López E, Rodríguez-Ascariz J. EOG-based eye movements codification for human computer interaction. Expert Systems with Applications 2012;39(3):2677–2683.  https://doi.org/10.1016/j.eswa.2011.08.123.CrossRefGoogle Scholar
  45. 45.
    Huang Q, He S, Wang Q, Gu Z, Peng N, Li K, Zhang Y, Shao M, Li Y. 2017. An EOG based human machine interface for wheelchair control. IEEE Trans Biomed Eng, pp 1–1.  https://doi.org/10.1109/tbme.2017.2732479.CrossRefGoogle Scholar
  46. 46.
    Heo J, Yoon H, Park K. A novel wearable forehead EOG measurement system for human computer interfaces. Sensors 2017;17(7):1485.  https://doi.org/10.3390/s17071485.CrossRefGoogle Scholar
  47. 47.
    Ang AMS, Zhang Z, Hung YS, Mak JNF. A user-friendly wearable single-channel EOG-based human-computer interface for cursor control. 2015 7th international IEEE/EMBS conference on neural engineering (NER). IEEE; 2015.  https://doi.org/10.1109/ner.2015.7146685.
  48. 48.
    Guo X, Pei W, Wang Y, Chen Y, Zhang H, Wu X, Yang X, Chen H, Liu Y, Liu R. A human-machine interface based on single channel EOG and patchable sensor. Biomed Signal Process Control 2016;30:98–105.  https://doi.org/10.1016/j.bspc.2016.06.018.CrossRefGoogle Scholar
  49. 49.
    Ning B, Li M, Liu T, Shen H, Hu L, Fu X. Human brain control of electric wheelchair with eye-blink electrooculogram signal. Intelligent robotics and applications. Berlin: Springer; 2012. p. 579–588.  https://doi.org/10.1007/978-3-642-33509-958.
  50. 50.
    Iturrate I, Antelis J, Minguez J. Synchronous EEG brain-actuated wheelchair with automated navigation. 2009 IEEE international conference on robotics and automation. IEEE; 2009.  https://doi.org/10.1109/robot.2009.5152580.
  51. 51.
    Wolpaw J, Ramoser H, McFarland D, Pfurtscheller G. EEG-based communication: improved accuracy by response verification. IEEE Trans Rehabil Eng 1998;6(3):326–333.CrossRefGoogle Scholar
  52. 52.
    Choudhari AM, Porwal P, Jonnalagedda V, Mériaudeau F. An Electrooculography based Human Machine Interface for wheelchair control. Biocybernetics and Biomedical Engineering. Elsevier BV. 2019.  https://doi.org/10.1016/j.bbe.2019.04.002.CrossRefGoogle Scholar

Copyright information

© IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringShri Guru Gobind Singhji Institute of Engineering & TechnologyVishnupuriIndia

Personalised recommendations