SSVEP-based brain-computer interface for computer control application using SVM classifier

  • Authors

    • Raymond Sutjiadi Informatics Department, Faculty of Information Technology, Institut Informatika Indonesia Surabaya
    • Timothy John Pattiasina Information System Department, Faculty of Information Technology, Institut Informatika Indonesia Surabaya
    • Resmana Lim Electrical Engineering Department, Faculty of Industrial Technology, Petra Christian University Surabaya
    2018-09-26
    https://doi.org/10.14419/ijet.v7i4.16139
  • Brain Computer Interface, Brain Waves, Electroencephalography, Steady State Visually Evoked Potential, Support Vector Machine.

  • Abstract

    In this research, a Brain Computer Interface (BCI) based on Steady State Visually Evoked Potential (SSVEP) for computer control applications using Support Vector Machine (SVM) is presented. For many years, people have speculated that electroencephalographic activities or other electrophysiological measures of brain function might provide a new non-muscular channel that can be used for sending messages or commands to the external world. BCI is a fast-growing emergent technology in which researchers aim to build a direct channel between the human brain and the computer. BCI systems provide a new communication channel for disabled people. Among many different types of the BCI systems, the SSVEP based has attracted more attention due to its ease of use and signal processing. SSVEPs are usually detected from the occipital lobe of the brain when the subject is looking at a twinkling light source. In this paper, SVM is used to classify SSVEP based on electroencephalogram data with proper features. Based on the experiment utilizing a 14-channel Electroencephalography (EEG) device, 80 percent of accuracy can be reached by our SSVEP-based BCI system using Linear SVM Kernel as classification engine.

     

  • References

    1. [1] Wolpaw JR, Birbaumer N, McFarlanda DJ, Pfurtscheller G & Vaughan TM, “Braincomputer Interfaces for Communication and Controlâ€, Clinical Neurophysiology, Vol. 113, (2002), pp. 767-791. https://doi.org/10.1016/S1388-2457(02)00057-3.

      [2] Yasin TV, Pasila F & Lim R, “A Study of Mobile Robot Control Using EEG Emotiv Epoc Sensorâ€, MATEC Web of Conferences, 164, (2018), art. No. 01044.

      [3] Rao R & Scherer R, “Brain-Computer Interfacing [in the Spotlight] Signal Process Magâ€, IEEE, Vol. 27, No. 4, (2010), pp. 152-150.

      [4] Erp JV, Lotte F & Tangermann M, “Brain-Computer Interfaces: Beyond Medical Applications Computerâ€, IEEE, Vol. 45, No. 4, (2012), pp. 26-34.

      [5] Bhattacharya BS, Gotarredona TS, Balassa L, Bhattacharya A, Stokes AB, Rowley A, Sugiarto I, & Furber S, “A Spiking Neural Network Model of the Lateral Geniculate Nucleus on the SpiNNaker Machineâ€, Frontiers in Neuroscience 11 (2017): 454. https://doi.org/10.3389/fnins.2017.00454.

      [6] Resalat SN, et al, “High-Speed SSVEP-Based BCI: Study of Various Frequency Pairs and Inter-Sources Distancesâ€, IEEE-EMBS International Conference Biomedical and Health Informatics, (2012), pp. 220 – 223.

      [7] Wang W, Degenhart AD, Sudre GP, Pomerleau DA &Tyler-Kabara EC. “Decoding Semantic Information from Human Electrocorticographic (Ecog) Signalsâ€, Engineering in Medicine and Biology Society (EMBC), (2011), pp. 6294–98.

      [8] Brumberg JS, Castanon AN, Kennedy PR & Guenther FH, “Brain-Computer Interfaces for Speech Communicationâ€, Speech Commun, Vol. 52, No. 4, (2010), pp. 367-379. https://doi.org/10.1016/j.specom.2010.01.001.

      [9] Vourvopoulos A & Liarokapis F, “Robot Navigation Using Brain-Computer Interfacesâ€, 11th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), (2012), pp. 1785–1792.

      [10] Alamdri N, Haider A, Arefin R, Verma AK & Tavakolian K, “A Review of Methods and Applications of Brain Computer Interface Systemsâ€, IEEE International Conference on Electro Information Technology (EIT), (2016), pp. 345-350.

      [11] Amiri S, Rabbi A, Azinfar L & Rezai RF, "A Review of P300, SSVEP, and Hybrid P300/SSVEP Brain-Computer Interface Systems", Brain-Computer Interface Systems – Recent Progress and Future Prospects, Rezai RF, Ed: In-Tech, (2013).

      [12] Allison B, Luth T, Valbuena D, Teymourian A, Volosyak I, & Graser A, "BCI Demographics: How many (and what kinds of) people can use an SSVEP BCI?", IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 18, (2010), pp. 107-116. https://doi.org/10.1109/TNSRE.2009.2039495.

      [13] Setiono T, Handojo A, Intan R, Sutjiadi R & Lim R, “Brain Computer Interface for Controlling RC-Car Using Emotiv Epoc+â€, Journal of Telecommunication, Electronic and Computer Engineering, Vol. 10 (2-3), (2018), pp. 169-172.

      [14] Rezai RF & Peters J, “P300 wave feature extraction: preliminary resultsâ€, Canadian Conference on Electrical and Computer Engineering, (2005), pp. 390-393.

      [15] Rezai RF, "P300-based Speller Brain-Computer Interfaceâ€, Recent Advances in Biomedical Engineering, Naik GR, Ed., In-Tech, (2009), pp. 137-148.

      [16] Rezai RF, Gavett S, Ahmad W, Rabbi A, & Schneider E, "A Comparison Among Several P300 Brain-Computer Interface Speller Paradigms", Clin EEG Neurosci, Vol. 42, (2011), pp. 209-213. https://doi.org/10.1177/155005941104200404.

      [17] Rezai RF, "Human Error in P300 Speller Paradigm for BrainComputer Interface", Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), (2007), pp. 2516-2519.

      [18] Sellers E & Donchin E, “A P300-Based Brain–Computer Interface: Initial Tests by ALS Patientsâ€, Clin Neurophysiol, Vol. 117, No. 3, (2006), pp. 538–548. https://doi.org/10.1016/j.clinph.2005.06.027.

      [19] Leocani L, Toro C, Zhuang P, Gerloff C & Hallett M, “Event-Related Desynchronization in Reaction Time Paradigms: a Comparison with Event-Related Potentials and Corticospinal Excitabilityâ€, Clinical Neurophysiology, Vol. 112, (2001), pp. 923–930. https://doi.org/10.1016/S1388-2457(01)00530-2.

      [20] Cheng M, Gao XR, Gao SK & Xu D, “Design and Implementation of a Brain Computer Interface with High Transfer Ratesâ€, IEEE Trans Biomed Engineering, Vol. 49, No. 10, (2002), pp. 1181-1186. https://doi.org/10.1109/TBME.2002.803536.

      [21] Wilson J & Palaniappan R, Analogue Mouse Pointer Control via an Online Steady State Visual Evoked Potential (SSVEP) Brain–Computer Interface, IOP Publishing, (2011), pp 1-6.

      [22] Sugiarto I, Allison B & Gräser A. “Optimization Strategy for SSVEP-based BCI in Spelling Program Applicationâ€, International Conference on Computer Engineering and Technology 2009 (ICCET'09), Vol. 1, (2009), pp. 223-226.

      [23] Jia C, Gao X, Hong B & Gao S, “Frequency and Phase Mixed Coding in SSVEP-Based Brain–Computer Interfaceâ€, IEEE Transactions on Biomedical Engineering, Vol. 58, (2011), pp. 200–206. https://doi.org/10.1109/TBME.2010.2068571.

      [24] Guger C, Allison B, Grosswindhager B, Pruckl RR, Hintermuller C, Kapeller C, Bruckner M, Krausz G & Edlinger G, “How Many People Could Use an SSVEP BCI?â€, Frontiers in Neuroscience, Vol. 6, No. 169, (2012). https://doi.org/10.3389/fnins.2012.00169.

      [25] Rajesh S & Haseena B, “Comparison of SSVEP Signal Classification Techniques Using SVM and ANN Models for BCI Applicationsâ€, International Journal of Information and Electronics Engineering, Vol. 4, (2014).

      [26] Cortes C & Vapnik V. “Support Vector Networksâ€, Machine Learning, Vol 20, (1995), pp. 273–297. https://doi.org/10.1007/BF00994018.

      [27] Burges CJC, “A Tutorial on Support Vector Machines for Pattern Recognitionâ€, Data Mining Knowledge Discovery, Vol. 2, No. 2, (1998), pp. 1–47. https://doi.org/10.1023/A:1009715923555.

      [28] Cristianini N & Taylor JS, An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, Cambridge University Press, (2000). https://doi.org/10.1017/CBO9780511801389.

      [29] Moore AW, Support Vector Machines, Tutorial Slides, (2003), pp. 2–10, https://www.autonlab.org/tutorials/svm.html.

      [30] Mitchell T, Machine Learning, McGraw-Hill Computer Science Series, (1997), pp. 86-88.

  • Downloads

  • How to Cite

    Sutjiadi, R., John Pattiasina, T., & Lim, R. (2018). SSVEP-based brain-computer interface for computer control application using SVM classifier. International Journal of Engineering & Technology, 7(4), 2722-2728. https://doi.org/10.14419/ijet.v7i4.16139

    Received date: 2018-07-27

    Accepted date: 2018-09-21

    Published date: 2018-09-26