Ⅴ. Contribution and Future Works
5.2 Future Works
More experiments must be carried out in order to obtain the full picture of foveal vs.
extrafoveal SSVEP responses. First, we indeed learn more about the effects of pulse
width and intensity towards the responses. Mesopic responses would be worth exploring.
Finally, we shall study the high-frequency and colored SSVEP responses of parafovea
and perifovea in order to map out the VEP characteristics of central retina.
Appendix
Table 5. Individual fovea SNRs of 3 channelsRing Frequency Table 6. Individual extrafoveal SNRs of 3 channels
Table 7. Individual fovea flickering scores
Center LFC KEL CGC JKZ TKC KHY CCC YYC
Frequency Exp.1 Exp.2 Exp.1 Exp.2 Exp.1 Exp.2 Exp.1 Exp.2 Exp.1 Exp.2 Exp.1 Exp.2 Exp.1 Exp.2 Exp.1 Exp.2
5 Hz 5 5 3 3 4 5 5 5 5 5 5 3 5 5 5 5
10 Hz 5 5 4 4 3 4 4 4 5 4 4 3 5 5 5 5
15 Hz 5 5 4 5 4 4 4 4 5 4 3 2 5 4 4 4
20 Hz 5 5 2 4 2 3 3 3 4 3 2 2 4 5 4 4
25 Hz 4 4 2 3 2 2 3 3 3 3 2 2 4 4 4 4
30 Hz 4 4 3 2 2 2 3 3 3 2 2 2 3 4 4 4
35 Hz 3 3 2 2 1 1 2 2 2 2 3 2 3 3 3 4
40 Hz 2 2 1 1 2 1 2 2 3 2 2 1 3 2 4 3
45 Hz 2 1 1 1 1 1 1 1 2 2 1 1 2 2 3 3
50 Hz 1 1 1 1 1 1 1 1 2 1 1 1 2 2 2 2
55 Hz 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
60 Hz 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
65 Hz 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Table 8. Individual extrafoveal flickering scores
Ring LFC KEL CGC JKZ TKC KHY CCC YYC
Frequency Exp.1 Exp.2 Exp.1 Exp.2 Exp.1 Exp.2 Exp.1 Exp.2 Exp.1 Exp.2 Exp.1 Exp.2 Exp.1 Exp.2 Exp.1 Exp.2
5 5 5 4 4 5 5 5 5 5 5 5 5 5 5 5 5
10 5 5 5 4 4 5 5 5 5 5 4 3 5 5 5 5
15 5 5 3 3 4 4 5 5 5 5 3 3 5 5 5 5
20 5 5 3 2 3 3 4 4 4 4 3 2 4 4 5 4
25 4 4 2 3 3 3 3 4 3 3 2 3 4 3 5 4
30 4 4 4 3 3 3 3 4 3 3 2 2 3 4 4 4
35 3 3 2 2 2 2 3 3 2 2 1 2 3 4 4 4
40 3 3 2 1 2 2 3 2 2 2 2 1 3 3 3 4
45 2 2 2 1 2 2 1 2 2 2 2 1 2 3 3 3
50 1 2 1 1 2 1 1 1 3 1 1 1 2 2 2 2
55 1 1 1 1 1 1 1 1 1 1 1 2 1 1 2 2
60 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
65 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Reference
1. Regan, D., Some characteristics of average steady-state and transient responses evoked by modulated light. Electroencephalography and Clinical Neurophysiology, 1966. 20(3): p. 238-248.
2. Regan, D., Human brain electrophysiology: evoked potentials and evoked magnetic fields in science and medicine. 1989.
3. Beverina, F., et al., User adaptive BCIs: SSVEP and P300 based interfaces.
PsychNology Journal, 2003. 1(4): p. 331-354.
4. Lee, P.L., et al., Visual evoked potential actuated brain computer interface: a brain-actuated cursor system. Electronics letters, 2005. 41(15): p. 832-834.
5. Jeavons, P.M. and G.F.A. Harding, Photosensitive epilepsy: a review of the literature and a study of 460 patients1975: Heinemann [for] Spastics International Medical Publications.
6. DeTommaso, M., et al., Steady‐state visual‐evoked potentials in headache:
diagnostic value in migraine and tension‐type headache patients. Cephalalgia, 1999. 19(1): p. 23-26.
7. Wolpaw, J.R., et al., Brain-computer interfaces for communication and control.
Clinical neurophysiology, 2002. 113(6): p. 767-791.
8. Nijholt, A., BCI for games: a ‘state of the art’survey. Entertainment Computing-ICEC 2008, 2009: p. 225-228.
9. Bashashati, A., et al., A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals. Journal of Neural Engineering, 2007. 4: p. R32-R57.
10. Lebedev, M.A. and M.A.L. Nicolelis, Brain-machine interfaces: past, present and future. TRENDS in Neurosciences, 2006. 29(9): p. 536-546.
11. Middendorf, M., et al., Brain-computer interfaces based on the steady-state visual-evoked response. Rehabilitation Engineering, IEEE Transactions on, 2000.
8(2): p. 211-214.
12. Cheng, M., et al., Design and implementation of a brain-computer interface with high transfer rates. Biomedical Engineering, IEEE Transactions on, 2002.
49(10): p. 1181-1186.
13. Wang, Y., et al., A practical VEP-based brain-computer interface. Neural Systems and Rehabilitation Engineering, IEEE Transactions on, 2006. 14(2): p.
234-240.
14. Sutter, E.E., The brain response interface: communication through
visually-induced electrical brain responses. Journal of Microcomputer Applications, 1992. 15(1): p. 31-45.
15. Gao, X., et al., A BCI-based environmental controller for the motion-disabled.
Neural Systems and Rehabilitation Engineering, IEEE Transactions on, 2003.
11(2): p. 137-140.
16. Lalor, E.C., et al., Steady-state VEP-based brain-computer interface control in an immersive 3D gaming environment. EURASIP journal on applied signal processing, 2005. 2005: p. 3156-3164.
17. Müller-Putz, G.R., et al., Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components. Journal of Neural Engineering, 2005. 2: p. 123.
18. Kelly, S.P., et al., Visual spatial attention control in an independent brain-computer interface. Biomedical Engineering, IEEE Transactions on, 2005.
52(9): p. 1588-1596.
19. Trejo, L.J., R. Rosipal, and B. Matthews, Brain-computer interfaces for 1-D and 2-D cursor control: designs using volitional control of the EEG spectrum or steady-state visual evoked potentials. Neural Systems and Rehabilitation Engineering, IEEE Transactions on, 2006. 14(2): p. 225-229.
20. Pan, J., et al., Enhancing the classification accuracy of steady-state visual evoked potential-based brain–computer interfaces using phase constrained canonical correlation analysis. Journal of Neural Engineering, 2011. 8: p.
036027.
21. Bin, G., et al., VEP-based brain-computer interfaces: time, frequency, and code modulations [Research Frontier]. Computational Intelligence Magazine, IEEE, 2009. 4(4): p. 22-26.
22. Herrmann, C.S., Human EEG responses to 1–100 Hz flicker: resonance phenomena in visual cortex and their potential correlation to cognitive phenomena. Experimental Brain Research, 2001. 137(3): p. 346-353.
23. Zhu, D., et al., A survey of stimulation methods used in SSVEP-based BCIs.
Computational intelligence and neuroscience, 2010. 2010: p. 1.
24. Parini, S., et al., A robust and self-paced BCI system based on a four class SSVEP paradigm: algorithms and protocols for a high-transfer-rate direct brain communication. Computational intelligence and neuroscience, 2009. 2009: p.
1-11.
25. Wang, Y.T., Y. Wang, and T.P. Jung, A cell-phone-based brain–computer interface for communication in daily life. Journal of Neural Engineering, 2011. 8:
p. 025018.
26. Calhoun, G.L. and G.R. McMillan. EEG-based control for human-computer
interaction. in Human Interaction with complex Systems. HCIS'96. 1996. Dayton, OH: IEEE.
27. Cheng, M., et al. Multiple color stimulus induced steady state visual evoked potentials. in Proceedings of the 23rd Annual EMBS International Conference.
2001. Istanbul,Turkey: IEEE.
28. Maggi, L., et al. A four command BCI system based on the SSVEP protocol. in Engineerting in Medicine and Biology Society. EMBS '06. 2006. IEEE.
29. Materka, A. and M. Byczuk. Using comb filter to enhance SSVEP for BCI applications. in IET 3rd international Conference on. 2006. Glasgow, UK: IET.
30. Piccini, L., et al. A wearable home BCI system: preliminary results with SSVEP protocol. in Proceedings of the 27rd Annual EMBS International Conference.
2006. Shanghai, China: IEEE.
31. Leow, R., F. Ibrahim, and M. Moghavvemi. Development of a steady state visual evoked potential (SSVEP)-based brain computer interface (BCI) system. in International Conference on Intelligent and Advanced Systems. 2007. IEEE.
32. Luth, T., et al. Low level control in a semi-autonomous rehabilitation robotic system via a brain-computer interface. in Proceedings of the 2007 IEEE 10th International Conference on Rehabilitation Robotics. 2007. Noordwijk, The Netherlands: IEEE.
33. Materka, A., M. Byczuk, and P. Poryzala. A virtual keypad based on alternate half-field stimulated visual evoked potentials. in 2007 International Symposium on Information Technology Convergence. 2007. IEEE.
34. Valbuena, D., et al. Brain-computer interface for high-level control of rehabilitation robotic systems. in Proceedings of the 2007 IEEE 10th International Conference on Rehabilitation Robotics. 2007. Noordwijk, The Netherlands: IEEE.
35. Garcia, G. High frequency SSVEPs for BCI applications. in Computer-Human Interaction. 2008. Citeseer.
36. Kelly, S.P., et al., Visual spatial attention tracking using high-density SSVEP data for independent brain-computer communication. Neural Systems and Rehabilitation Engineering, IEEE Transactions on, 2005. 13(2): p. 172-178.
37. Lin, Z., et al., Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs. Biomedical Engineering, IEEE Transactions on, 2006.
53(12): p. 2610-2614.
38. Nielsen, K.D., A.F. Cabrera, and O.F. do Nascimento, EEG based BCI-towards a better control. Brain-computer interface research at Aalborg university. Neural Systems and Rehabilitation Engineering, IEEE Transactions on, 2006. 14(2): p.
202-204.
39. Wu, Z., et al., Stimulator selection in SSVEP-based BCI. Medical engineering &
physics, 2008. 30(8): p. 1079-1088.
40. Sugiarto, I., B. Allison, and A. Graser. Optimization strategy for SSVEP-based BCI in spelling program application. in International Conference on Computer Engineering and Technology. 2009. IEEE.
41. Yijun, W., et al. Brain-computer interface based on the high-frequency steady-state visual evoked potential. in First International Conference on neural Interface and Control Proceedigs. 2005. IEEE.
42. Mandel, C., et al. Navigating a smart wheelchair with a brain-computer interface interpreting steady-state visual evoked potentials. in IEEE/RSJ International Conference on Intelligent Robots and Systems. 2009. Louis, USA:
IEEE.
43. Pun, T., et al., Brain-computer interaction research at the Computer Vision and Multimedia Laboratory, University of Geneva. Neural Systems and Rehabilitation Engineering, IEEE Transactions on, 2006. 14(2): p. 210-213.
44. Hecht, Optics. 3rd ed, Addision Wesley.
45. Millodot, M., Dictionary of Optometry and Visual Science, 7th edition. 7th ed:
Butterworth-Heinemann.
46. Iwasaki, M. and H. Inomata, Relation between superficial capillaries and foveal structures in the human retina. Invest Ophthalmol Vis Sci, 1986. 27(12): p.
1698-1705.
47. Hecht, S. and S. Shlaer, Intermittent stimulation by light. The Journal of general physiology, 1936. 19(6): p. 965-977.
48. Hakvoort, G., B. Reuderink, and M. Obbink, Comparison of PSDA and CCA detection methods in a SSVEP-based BCI-system, 2011, Centre for Telematics and Information Technology University of Twente, Enschede.
49. Bin, G., et al., An online multi-channel SSVEP-based brain–computer interface using a canonical correlation analysis method. Journal of Neural Engineering, 2009. 6: p. 046002.
50. Bin, G., et al., A high-speed BCI based on code modulation VEP. Journal of Neural Engineering, 2011. 8: p. 025015.
51. Rietveld, W.J., W.E. Tordoir, and J.W. Duyff, Contribution of fovea and parafovea to the visual evoked response. Acta Physiol Pharmacol Neerl, 1965.
13(3): p. 330-9.
52. Wang, Y., et al., Brain-computer interfaces based on visual evoked potentials.
Engineering in Medicine and Biology Magazine, IEEE, 2008. 27(5): p. 64-71.