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An Efficient Classifier for P300 in Brain–Computer Interface Based on Scalar Products

  • Monica FiraEmail author
  • Liviu Goras
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)

Abstract

In this paper, a simple but efficient method for detection of P300 waveform in a Brain–Computer Interface (BCI) is presented. The proposed method is based on computing scalar products between the waveforms to be classified and a P300 pattern. Depending on the degree of concentration of the subject and the number of trails, rates of recognition between 85 and 100% have been obtained.

Keywords

BCI EEG signal Signal processing P300 Classification 

Notes

Acknowledgements

We want to thank all human subjects who have voluntarily participated in experiment and Ulrich Hoffmann and his team for permission to use the EEG data available on the Internet.

References

  1. 1.
    Birbaumer N, Ghanayim N, Hinterberger T, Iversen I, Kotchoubey B, Kübler A, Perelmouter J, Taub E, Flor H (1999) A spelling device for the paralyzed. Nature 398:297–298CrossRefGoogle Scholar
  2. 2.
    Mason SG, Birch GE (2000) A brain-controlled switch for asynchronous control applications. IEEE Trans Biomed Eng 47:1297–1307CrossRefGoogle Scholar
  3. 3.
    Pfurtscheller G, Flotzinger D, Kalcher J (1993) Brain-computer interface: a new communication device for handicapped persons. J Microcomput Appl 16:293–299CrossRefGoogle Scholar
  4. 4.
    Wolpaw JR, McFarland DJ, Neat GW, Forneris CA (1991) An EEG-based brain-computer interface for cursor control. Electroencephalogr Clin Neurophysiol 78:252–259CrossRefGoogle Scholar
  5. 5.
    Jones KS, Middendorf M, McMillan GR, Calhoun G, Warm J (2003) Comparing mouse and steady-state visual evoked response-based control. Interact Comput 15:603–621CrossRefGoogle Scholar
  6. 6.
    Farwell LA, Donchin E (1988) Talking off the top of your head: a mental prosthesis utilizing event-related potentials. Electroencephalogr Clin Neurophysiol 70:510–523CrossRefGoogle Scholar
  7. 7.
    Donchin E, Spencer K, Wijesinghe R (2000) The mental prosthesis: assessing the speed of a P300-based brain-computer interface. IEEE Trans Rehab Eng 8(2)CrossRefGoogle Scholar
  8. 8.
    Sutton S, Braren M, Zubin J, John ER (1965) Evoked correlates of stimulus uncertainty. Science 150:1187–1188CrossRefGoogle Scholar
  9. 9.
    Donchin E (1980) Presidential address. Surprise!…Surprise? Psychophysiology 18:493–513Google Scholar
  10. 10.
    Fabiani M, Gratton G, Karis D, Donchin E (1987) Definition, identification and reliability of the P300 component of the event-related brain potential. In: Ackles PK, Jennings JR, Coles MGH (eds) Advances in psychophysiology, vol 2. JAI Press, New York, pp 1–78Google Scholar
  11. 11.
    Polich J (1999) P300 in clinical applications. In: Niedermeyer E, Lopes da Silva FH (eds) Electroencephalography: basic principles, clinical applications and related fields, 4th edn. Williams and Wilkins, Baltimore, pp 1073–1091Google Scholar
  12. 12.
    Rosenfeld JP (1990) Applied psychophysiology and biofeedback of event-related potentials (brain waves): historical perspective, review, future directions. Biofeedback Self Regul 15:99–119CrossRefGoogle Scholar
  13. 13.
    Coles MGH, Rugg MD (1995) Event-related potentials: an introduction. In: Rugg MD, Coles MGH (eds) Electrophysiology of the mind: event-related brain potentials and cognition. Oxford University Press, New YorkGoogle Scholar
  14. 14.
    Kramer AF, Strayer DL (1988) Assessing the development of automatic processing: an application of dual-track and event-related brain potential methodologies. Biol Psychol 26:231–267CrossRefGoogle Scholar
  15. 15.
    O’Donnell BF, Friedman S, Swearer JM, Drachman DA (1992) Active and passive P3 latency and psychometric performance: influence of age and individual differences. Int J Psychophysiol 12:185–187CrossRefGoogle Scholar
  16. 16.
    Polich J (1986) Attention, probability, and task demands as determinants of P300 latency from auditory stimuli. Electroencephalogr Clin Neurophysiol 63:251–259CrossRefGoogle Scholar
  17. 17.
    Hoffmann U, Vesin JM, Ebrahimi T, Diserens K (2008) An efficient P300-based brain–computer interface for disabled subjects. J Neurosci Methods 167(1):115–125 (15 Jan 2008)CrossRefGoogle Scholar
  18. 18.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Computer ScienceRomanian AcademyIasiRomania
  2. 2.“Gheorghe Asachi” Technical University of IasiIasiRomania

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