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Usefulness of EGI EEG system in brain computer interface research

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Despite the quick development of medicine and associated medical technology, there are still many patients with very severe neurological deficits who need far more sophisticated solutions, such as brain computer interfaces (BCIs). Our research aims at becoming familiar with BCI technology and the assessment of the possibilities of selected subjects in the area of P300-based BCI. An indirect aim is a discussion on the procedures of patient preparation for BCI installation, possible threats and limitations. Our research presents one of the possible efforts towards better documentation of investigating neural correlates of brain processing.
Rocznik
Strony
73--79
Opis fizyczny
Bibliogr. 29 poz., zdj.
Twórcy
  • Institute of Computer Science, Maria Curie-Sklodowska University, Lublin, Poland
  • Rehabilitation Clinic, The 10th Military Clinical Hospital with Polyclinic, Bydgoszcz, Poland
  • Institute of Mechanics and Applied Computer Science, Kazimierz Wielki University, Bydgoszcz, Poland
  • Division of Applied Informatics, Nicolaus Copernicus University, Toruń, Poland
  • Institute of Computer Science, Maria Curie-Sklodowska University, Lublin, Poland
autor
  • Institute of Computer Science, Maria Curie-Sklodowska University, Lublin, Poland
autor
  • Institute of Computer Science, Maria Curie-Sklodowska University, Lublin, Poland
Bibliografia
  • 1. Pei X, Hill J, Schalk G. Silent communication: toward using brain signals. IEEE Pulse 2012;3:43–6.
  • 2. Shih JJ, Krusienski DJ, Wolpaw JR. Brain-computer interfaces in medicine. Mayo Clin Proc 2012;87:268–79.
  • 3. Daly JJ, Wolpaw JR. Brain-computer interfaces in neurological rehabilitation. Lancet Neurol 2008;7:1032–43.
  • 4. Allison BZ, Wolpaw EW, Wolpaw JR. Brain-computer interface systems: progress and prospects. Expert Rev Med Devices 2007;4:463–74.
  • 5. Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM. Brain-computer interfaces for communication and control. Clin Neurophysiol 2002;113:767–91.
  • 6. Rossini PM, Noris Ferilli MA, Ferreri F. Cortical plasticity and brain computer interface. Eur J Phys Rehabil Med 2012;48:307–12.
  • 7. Bashashati A, Fatourechi M, Ward RK, Birch GE. A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals. J Neural Eng 2007;4:R32–57.
  • 8. Wierzgała P, Wójcik GM. Finding the best efficiency in ActionScript based web applications on example of FFT algorithm. Bio-Algorithms Med-Syst 2012;8:373–85.
  • 9. EGI Geodesic EEG System (GES) 300. Available from: http://www.egi.com/research-division-research-products/eeg-systems. Accessed on 20 November, 2012.
  • 10. Hu X, Pornpattananangkul N, Rosenfeld JP. N200 and P300 as orthogonal and integrable indicators of distinct awareness and recognition processes in memory detection. Psychophysiology 2013;50:454–64.
  • 11. Pires G, Nunes U, Castelo-Branco M. Comparison of a row-column speller vs. a novel lateral single-character speller: assessment of BCI for severe motor disabled patients. Clin Neurophysiol 2012;123:1168–81.
  • 12. Fazel-Rezai R, Allison BZ, Guger C, Sellers EW, Kleih SC, Kübler A. P300 brain computer interface: current challenges and emerging trends. Front Neuroeng 2012;5:14.
  • 13. Guger C, Daban S, Sellers E, Holzner C, Krausz G, Carabalona R, et al. How many people are able to control a P300-based brain-computer interface (BCI)? Neurosci Lett 2009;462:94–8.
  • 14. Lehembre R, Gosseries O, Lugo Z, Jedidi Z, Chatelle C, Sadzot B, et al. Electrophysiological investigations of brain function in coma, vegetative and minimally conscious patients. Arch Ital Biol 2012;150:122–39.
  • 15. Kübler A, Birbaumer N. Brain-computer interfaces and communication in paralysis: extinction of goal directed thinking in completely paralysed patients? Clin Neurophysiol 2008;119:2658–66.
  • 16. Murguialday AR, Hill J, Bensch M, Martens S, Halder S, Nijboer F, et al. Transition from the locked in to the completely locked-in state: a physiological analysis. Clin Neurophysiol 2011;122: 925–33.
  • 17. Bruno MA, Vanhaudenhuyse A, Thibaut A, Moonen G, Laureys S. From unresponsive wakefulness to minimally conscious PLUS and functional locked-in syndromes: recent advances in our understanding of disorders of consciousness. J Neurol 2011;258:1373–84.
  • 18. Gosseries O, Schnakers C, Ledoux D, Vanhaudenhuyse A, Bruno MA, Demertzi A, et al. Automated EEG entropy measurements in coma, vegetative state/unresponsive wakefulness syndrome and minimally conscious state. Funct Neurol 2011;26:25–30.
  • 19. McFarland DJ, Sarnacki WA, Wolpaw JR. Should the parameters of a BCI translation algorithm be continually adapted? J Neurosci Methods 2011;199:103–7.
  • 20. Breshears JD, Gaona CM, Roland JL, Sharma M, Anderson NR, Bundy DT, et al. Decoding motor signals from the pediatric cortex: implications for brain-computer interfaces in children. Pediatrics 2011;128:e160–8.
  • 21. Roland J, Miller K, Freudenburg Z, Sharma M, Smyth M, Gaona C, et al. The effect of age on human motor electrocorticographic signals and implications for brain-computer interface applications. J Neural Eng 2011;8:046013.
  • 22. Wireless Ultra Low Power Broadband Neural Recording Microsystem, Brown University. Available from: http://nurmikko.engin.brown.edu/?q=node/1. Accessed on 20 November, 2012.
  • 23. Heinz A, Kipke R, Heimann H, Wiesing U. Cognitive neuroenhancement: false assumptions in the ethical debate. J Med Ethics 2012;38:372–5.
  • 24. Shaw DM. Neuroenhancers, addiction and research ethics. J Med Ethics 2012;38:605–8.
  • 25. Warvick K. I, cyborg. Champaign: University of Illinois Press, 2004.
  • 26. van den Brand R, Heutschi J, Barraud Q, DiGiovanna J, Bartholdi K, Huerlimann M, et al. Restoring voluntary control of locomotion after paralyzing spinal cord injury. Science 2012;336:1182–5.
  • 27. Domenici N, Keller U, Vallery H, Friedli L, van den Brand R, Starkey ML, et al. Versatile robotic interface to evaluate, enable and train locomotion and balance after neuromotor disorders. Nat Med 2012;18:1142–7.
  • 28. Ruffini G, Dunne S, Farrés E, Watts PC, Mendoza E, Silva SR, et al. ENOBIO – First tests of a dry electrophysiology electrode using carbon nanotubes. In: Proceedings of 28th Annual International Conference of the IEEE, August 2006, pp. 1826–9.
  • 29. Durka PJ. Matching pursuit and unification in EEG analysis. Boston, London: Artech House, 2007.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d68d499c-a8bb-4ae3-9b74-d283001baef0
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