Search
Menu
All issues Volume 3 / No 1 (December 2015) EPJ Nonlinear Biomed Phys, 3 1 (2015) 9 References
Open Access
Issue
EPJ Nonlinear Biomed Phys
Volume 3, Number 1, December 2015
Article Number 9
Number of page(s) 17
DOI https://doi.org/10.1140/epjnbp/s40366-015-0024-2
Published online 28 October 2015
  1. Dornhege G, Millan J, Hinterberger T, McFarland DJ, Müller KR. Toward brain-computer interfacing. Cambridge: MIT Press; 2007. [Google Scholar]
  2. Wolpaw JR, Birbaumer N, Heetderks WJ, McFarland DJ, Peckham PH, Schalk G, et al.Brain-computer interface technology: a review of the first international meeting. IEEE Trans Neural Syst Rehabil Eng. 2000; 8(2):164–73. [Google Scholar]
  3. Birbaumer N, Ghanayim N, Hinterberger T, Iversen I, Kotchoubey B, Kübler A, et al.A spelling device for the paralysed. Nature. 1999; 398:297–8. [Google Scholar]
  4. 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. [Google Scholar]
  5. Kübler A. Brain-computer interfacing: science fiction has come true. Brain. 2013; 136:2001–4. [Google Scholar]
  6. Sellers EW, Donchin E. A P300-based brain-computer interface: initial tests by ALS patients.Clin Neurophysiol. 2006; 117:538–48. [Google Scholar]
  7. Wolpaw JR, Birbaumer N, Pfurtscheller G, Mcfarland DJ, Vaughan TM. Brain-computer interfaces for communication and control. Clin Neurophysiol. 2002; 6:767–91. [Google Scholar]
  8. Del R Millan JJ, Galan F, Vanhooydonck D, Lew E, Philips J, Nuttin M. Asynchronous non-invasive brain-actuated control of an intelligent wheelchair. In: Conf. Proc. of the 31st IEEE Eng. Med. Biol. Soc. USA. Minnesota: Hilton Minneapolis: 2009. p. 3361–4. [Google Scholar]
  9. Mohebbi A, Engelsholm SK, Puthusserypady S, Kjaer TW, Thomsen CE, Sorensen HBD. A brain computer interface for robust wheelchair control application based on pseudorandom code modulated visual evoked potential. In: Proc. of the 37th Intl. Conf. of the IEEE Eng. Med. Biol. Soc. Milan, Italy: 2015. [Google Scholar]
  10. Ali A, Puthusserypady S. A 3D learning playground for potential attention training in ADHD: A brain computer interface approach. In: Proc. of the 37th Intl. Conf. of the IEEE Eng. Med. Biol. Soc. Milan, Italy: 2015. [Google Scholar]
  11. Daly JJ, Wolpaw JR. Brain-computer interfaces in neurological rehabilitation. Lancet Neurol. 2008; 7:1032–43. [Google Scholar]
  12. Blankertz B, Tangermann M, Vidaurre C, Fazli S, Sannelli C, Haufe S, et al.The Berlin brain-computer interface: non-medical uses of BCI technology. Front Neuroscience. 2010; 4(Article 198):1–17. [Google Scholar]
  13. Müller KR, Tangermann M, Dornhege G, Krauledat M, Curio G, Blankertz B. Machine learning for real-time single-trial EEG-analysis: from brain-computer interfacing to mental state monitoring. Jl Neurosci Methods. 2007; 167(1):82–90. [Google Scholar]
  14. Serby H, Yom-Tov E, Inbar GF. An improved P300-based brain-computer interface. IEEE Trans Neural Syst Rehabil Eng. 2005; 13(1):89–98. [Google Scholar]
  15. Lugo ZR, Rodriguez J, Lechner A, Ortner R, Gantner IS, Laureys S, et al.A vibrotactile p300-based brain-computer interface for consciousness detection and communication. Clin EEG Neurosci. 2014; 45(1):14–21. [Google Scholar]
  16. 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. Clin Neurophysiol. 2006; 117(3):531–7. [Google Scholar]
  17. Farwell LA, Donchin E. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroenceph Clin Neurophysiol. 1988; 70(6):510–23. [Google Scholar]
  18. Bayliss JD. Use of the evoked potential P3 component for control in a virtual apartment. IEEE Trans Neural Syst Rehabil Eng. 2003; 11(2):113–6. [Google Scholar]
  19. Vilic A, Kjaer TW, Thomsen CE, Puthusserypady S, Sorensen HBD. DTU BCI speller: an SSVEP-based spelling system with dictionary support. In: Proc. of the 35th IEEE Eng. Med. Biol. Soc. Osaka, Japan: 2013. p. 2212–5. [Google Scholar]
  20. Ortner R, Allison B, Korisek G, Gaggl H, Pfurtscheller G. An SSVEP BCI to control a hand orthosis for persons with Tetraplegia. IEEE Trans Neural Syst Rehabil Eng. 2011; 19(1):1–5. [Google Scholar]
  21. Leow R, Ibrahim F, Moghavvemi M. Development of a steady state visual evoked potential (SSVEP)-based brain computer interface (BCI) system. In: Intl. Conf. on Intell. and Adv. Syst. Kuala Lumpur: 2007. p. 321–4. [Google Scholar]
  22. Liavas AP, Moustakides GV, Henning G, Psarakis EZ, Husar P. A periodogram-based method for the detection of steady-state visually evoked potentials. IEEE Trans Biomed Eng. 1998; 45(2):242–8. [Google Scholar]
  23. Cheng M, Gao X, Gao S, Xu D. Design and implementation of a brain-computer interface with high transfer rates. IEEE Trans Biomed Eng. 2002; 49(10):1181–6. [Google Scholar]
  24. Wolpaw JR, McFarland DJ. Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. Proc Nat Acad Sci. 2004; 17:849–54. [Google Scholar]
  25. McFarland DJ, Wolpaw JR. Sensorimotor rhythm-based braincomputer interface (BCI): feature selection by regression improves performance. IEEE Trans Neural Syst Rehabil Eng. 2005; 13(3):372–9. [Google Scholar]
  26. Yue J, Zhou Z, Jiang J, Liu Y, Hu D. Balancing a simulated inverted pendulum through motor imagery: an EEG-based real-time control paradigm. Neurosci Lett. 2012; 524(2):95–100. [Google Scholar]
  27. Friedricha E, Schererb R, Neuper C. Long-term evaluation of a 4-class imagery-based brain-computer interface. Clin. Neurophysiol. 2013; 124(5):916–27. [Google Scholar]
  28. Hazrati M, Erfanian A. An online EEG-based brain-computer interface for controlling hand grasp using an adaptive probabilistic neural network. Med Eng Phys. 2010; 32(7):730–9. [Google Scholar]
  29. Faller J, Vidaurre C, Solis-Escalante T, Neuper C, Scherer R. Auto-calibration and recurrent adaptation: towards a plug and play online ERD-BCI. IEEE Trans Neural Syst Rehabil Eng. 2012; 20(3):313–9. [Google Scholar]
  30. Iversen IH, Ghanayim N, Kübler A, Neumann N, Birbaumer N, Kaiser J. A brain-computer interface tool to assess cognitive functions in completely paralyzed patients with amyotrophic lateral sclerosis. Clin Neurophysiol. 2008; 119(10):2214–23. [Google Scholar]
  31. Decety J. The neurophysiological basis of motor imagery. Behav Brain Res. 1996; 77(1-2):45–52. [Google Scholar]
  32. Pfurtscheller G, Lopes DSF. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol. 1999; 110(11):1842–1457. [Google Scholar]
  33. Royer A, He B. Goal selection versus process control in a brain-computer interface based on sensorimotor rhythms. Jl Neural Eng. 2009;6(1). doi:10.1088/1741--2560/6/1/016005. [Google Scholar]
  34. Blankertz B, Losch F, Krauledat M, Dornhege G, Curio G Müller KR. The Berlin brain-computer interface: accurate performance from first-session in BCI-Naïve Subjects. IEEE Trans on Biomed Eng. 2008; 55(10):2452–62. [Google Scholar]
  35. Vuckovic A, Sepulveda F. Quantification and visualisation of differences between two motor tasks based on energy density maps for brain-computer interface applications. Clin Neurophysiol. 2008; 119(2):446–58. [Google Scholar]
  36. Kübler A, Müller KR. An introduction to brain-computer interfacing. Cambridge: MIT Press, p. 2007. [Google Scholar]
  37. El-Madani A. Introduction to brain computer interface. Study report. Denmark: Dep. Elec. Eng., DTU, Lyngby;2009. [Google Scholar]
  38. Oldfield RC. The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia. 1971; 9(1):97–113. [Google Scholar]
  39. Müller-Gerking J, Pfurtscheller G, Flyvbjerg H. Designing optimal spatial filters for single-trial EEG classification in a movement task. Clin Neurophysiol. 1999; 110(5):787–98. [Google Scholar]
  40. Blankertz B, Tomioka R, Lemm S, Kawanabe M, Müller KR. Optimizing spatial filters for robust EEG single-trial analysis. IEEE Sig Process Mag. 2008; 25(1):41–56. [Google Scholar]
  41. Fukunaga K. Random vectors and their properties. In: Introduction to statistical pattern recognition. New York: Morgan Kaufmann Publishers: 1990. p. 10–35. [Google Scholar]
  42. Pedro D, Michael P. On the optimality of the simple Bayesian classifier uner zero-one loss. Mach Learn. 1997; 29:103–30. [Google Scholar]
  43. Jonsson M.Brain computer interface. MSc. Thesis. Denmark: Dep. Elec. Eng., DTU; 2008. [Google Scholar]
  44. Blumberg J, Rickert J, Waldert S, Schulze-Bonhage A, Aertsen A, Mehring C. 2007. Adaptive classification for brain computer interfaces, Vol. 1. France: Med. Biol. Soc. Conf. Lyon. [Google Scholar]
  45. Dickhaus T, Sannelli C, Müller KR, Curio G, Blankertz B. Predicting BCI performance to study BCI illiteracy. Bio. Med. Centr. Neuroscience. 2009; 10(Suppl 1):84. doi:10.1186/1471-2202-10-S1-P84. [Google Scholar]
  46. Vidaurre C, Blankertz B. Towards a cure for BCI illiteracy. Brain Topogr. 2010; 23(2):194–8. [Google Scholar]