Artificial Intelligence in Medicine
Volume 44, Issue 3 , Pages 247-259 , November 2008

Fusion of classic P300 detection methods’ inferences in a framework of fuzzy labels

  • Gholamreza Salimi-Khorshidi

      Affiliations

    • School of Cognitive Sciences (SCS), Institute for Studies in Theoretical Physics and Mathematics (IPM), Tehran, Iran
    • Oxford Centre for Functional MRI of the Brain, Department of Clinical Neurology, University of Oxford, FMRIB Centre, J R Hospital, Headley Way, Headington, Oxford OX3 9DU, UK
    • Iranian Center of Excellence in Biomedical Engineering, Faculty of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
    • Corresponding Author InformationCorresponding author at: Oxford Centre for Functional MRI of the Brain, Department of Clinical Neurology, University of Oxford, FMRIB Centre, J R Hospital, Headley Way, Headington, Oxford OX3 9DU, UK. Tel.: +44 1865 222729; fax: +44 1865 222717.
  • ,
  • Ali Motie Nasrabadi

      Affiliations

    • Group of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, Iran
  • ,
  • Mohammadreza Hashemi Golpayegani

      Affiliations

    • Iranian Center of Excellence in Biomedical Engineering, Faculty of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

Received 18 February 2007 ,Revised 15 June 2008 ,Accepted 16 June 2008.

References 

  1. Picton TW, Hillyard SA. Endogenous event-related potentials. In:  Picton TW editors. Handbook of electroencephalographic clinical neurophysiology. Amsterdam: Elsevier; 1988;p. 361–426
  2. Blankertz B, Müller KR, Curio G, Vaughan TM, Schalk G, Wolpaw JR, et al. The BCI Competition 2003: progress and perspectives in detection and discrimination of EEG single trials. IEEE Transactions on Biomedical Engineering. 2004;51:1044–1051
  3. Birbaumer N, Ghanayim N, Hinterberger T, Iversen I, Kotchoubey B, Kübler A, et al. A spelling device for the paralyzed. Nature. 1999;398:297–298
  4. Donchin E, Spencer KM, Wijesinghe R. The mental prosthesis: assessing the speed of a p300-based brain–computer interface. IEEE Transactions on Rehabilitation Engineering. 2000;8:174–179
  5. Farwell LA, Donchin E. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalography & Clinical Neurophysiology. 1988;70:510–523
  6. Donchin E, Spencer KM, Wijensinghe R. The mental prosthesis: assessing the speed of a P300-based brain–computer interface. IEEE Transactions on Rehabilitation Engineering. 2000;8:174–179
  7. Bayliss J, Ballard D. A virtual reality tested for brain computer interface research. IEEE Transactions on Rehabilitation Engineering. 2000;8:188–190
  8. Meinicke P, Kaper M, Hoppe F, Heumann M, Ritter H. Improving transfer rates in brain computer interfacing: a case study. In:  Becker S,  Thrun S,  Obermayer K editor. Advances in neural information processing systems. Cambridge, MA: MIT Press; 2003;p. 1107–1114
  9. J.R. Wolpaw, P300 speller paradigm, BCI Competition 2003, available online at: http://ida.first.fraunhofer.de/projects/bci/competition (last access 3 April 2008).
  10. Xu N, Gao X, Hong B, Miao X, Gao S, Yang F. BCI Competition 2003—data set IIb: enhancing P300 wave detection using ICA-based subspace projections for BCI applications. IEEE Transactions on Biomedical Engineering. 2004;51:1067–1072
  11. Kaper M, Meinicke P, Grossekathoefer U, Lingner T, Ritter H. BCI Competition 2003—data set IIb: support vector machines for the P300 speller paradigm. IEEE Transactions on Biomedical Engineering. 2004;51:1073–1076
  12. Bostanov V. BCI Competition 2003—data sets Ib and IIb: feature extraction from event-related brain potentials with the continuous wavelet transform and the t-value scalogram. IEEE Transactions on Biomedical Engineering. 2004;51:1057–1061
  13. Glassman EL. A wavelet-like filter based on neuron action potentials for analysis of human scalp electroencephalographs. IEEE Transactions on Biomedical Engineering. 2005;52:1851–1862
  14. Hoffmann U, Garcia G, Vesin J, Diserens K, Ebrahimi T. A boosting approach to P300 detection with application to brain–computer interfaces. In: Conference proceedings of the 2nd international IEEE EMBS conference on neural engineering, IEEE Cat. No. 05EX938, 2005, pp. 97–100.
  15. Garrett D, Peterson DA, Anderson CW, Thaut MH. Comparison of linear, nonlinear and feature selection methods for EEG signal classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2003;11:141–152
  16. Penny WD, Roberts SJ, Curran EA, Stokes MJ. EEG-based communication: a pattern recognition approach. IEEE Transactions on Rehabilitation Engineering. 2000;8:214–215
  17. Salimi Khorshidi G, Nasrabadi AM, Golpayegani MH. Modifying the classic template matching technique using a fuzzy multi-agent to have an accurate P300 detection. In:  Ibrahim F,  Abu Osman AN,  Usman J,  Kadri NA editor. Proceeding of the 3rd Kuala Lumpur international conference on biomedical engineering. Berlin: Springer; 2007;p. 410–414
  18. Jaskowski P, Verleger R. An evaluation of methods for single-trial estimation of P3 latency. Psychophysiology. 2000;37:153–162
  19. Luck SJ. An introduction to the event-related potential technique. Cambridge, MA: MIT press; 2005;
  20. Herman P, Prasad G, McGinnity TM. Investigation of the type-2 fuzzy logic approach to classification in an EEG-based brain–computer interface. In: Proceeding of the 27th annual international conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005. 2005;p. 5354–5357
  21. Palaniappan R, Paramesran R, Nishida S, Saiwaki N. A new brain–computer interface design using fuzzy ARTMAP. IEEE Transactions on Rehabilitation Engineering. 2002;10:140–148
  22. Karasavvas KA, Baldock R, Burger A. A criticality-based framework for task composition in multi-agent bioinformatics integration systems. Bioinformatics. 2005;21:3155–3163
  23. Richard N, Dojat M, Garbay C. Automated segmentation of human brain MR images using a multi-agent approach. Artif Intell Med. 2004;30:153–175
  24. Hoffmann U, Vesin JM, Ebrahimi T, Diserens K. An efficient P300-based brain–computer interface for disabled subjects. J Neurosci Methods. 2008;167:115–125
  25. Serby H, Yom-Tov E, Inbar GF. An improved P300-based brain–computer interface. IEEE Transactions on Rehabilitation Engineering. 2005;13:89–98
  26. Sellers EW, Krusienski DJ, McFarland DJ, Vaughan TM, Wolpaw JR. A P300 event-related potential brain–computer interface (BCI): the effects of matrix size and inter stimulus interval on performance. Biol Psychol. 2006;73:242–252
  27. Bai O, Lin P, Vorbach S, Li J, Furlani S, Hallett M. Exploration of computational methods for classification of movement intention during human voluntary movement from single trial EEG. Clinical Neurophysiology. 2007;118:2637–2655
  28. Vidaurre C, Schlögl A, Cabeza R, Scherer R, Pfurtscheller G. Study of on-line adaptive discriminant analysis for EEG-based brain computer interfaces. IEEE Transactions on Biomedical Engineering. 2007;54:550–556
  29. Sellers EW, Donchin E. A P300-based brain–computer interface: initial tests by ALS patients. Clinical Neurophysiology. 2006;117:538–548
  30. Piccione F, Giorgi F, Tonin P, Priftis K, Giove S, Silvoni S, et al. P300-based brain computer interface: reliability and performance in healthy and paralysed participants. Clinical Neurophysiology. 2006;117:531–537
  31. Beverina F, Palmas G, Silvoni S, Piccione F, Giove S. User adaptive BCIs: SSVEP and P300 based interfaces. PsychNology. 2003;1:331–354

PII: S0933-3657(08)00082-1

doi: 10.1016/j.artmed.2008.06.002

Artificial Intelligence in Medicine
Volume 44, Issue 3 , Pages 247-259 , November 2008