Model analyses of visual biofeedback training for EEG-based brain-computer interface

Journal of Computational Neuroscience

Volumn 27, Issue 3, 2009, Pages 357-368

  Chen, Chih Wei ^a   Ju, Ming Shaung ^a   Sun, Yun Nien ^a   Lin, Chou Ching K ^b  

^a NATIONAL CHENG KUNG UNIVERSITY   (Taiwan)

^b NATIONAL CHENG KUNG UNIVERSITY HOSPITAL   (Taiwan)

Author keywords

Biofeedback; Brain computer interface; Mu rhythm; Thalamo cortical model

Indexed keywords

BIOFEEDBACK; BRAIN; ESTIMATION; INTERFACES (COMPUTER); SIGNAL PROCESSING; TRACKING (POSITION);

CORTICAL MODELS; INTERFACE SYSTEM; MODELING ANALYZES; MODELING PARAMETERS; MU RHYTHM; PERFORMANCE; SIMULATION MODEL; THALAMO-CORTICAL MODEL; THREE MODELS; VISUAL TRACKING SYSTEMS;

BRAIN COMPUTER INTERFACE;

ADAPTATION; ADULT; ALGORITHM; ARTICLE; BRAIN COMPUTER INTERFACE; COMPUTER SIMULATION; CONTROLLED STUDY; ELECTROENCEPHALOGRAM; EYE TRACKING; FEEDBACK SYSTEM; HUMAN; HUMAN EXPERIMENT; MAN MACHINE INTERACTION; MATHEMATICAL MODEL; MU RHYTHM; NORMAL HUMAN; PRIORITY JOURNAL; SIGNAL PROCESSING; SYNAPSE; THALAMOCORTICAL TRACT; VISUAL INFORMATION;

EID: 70350571729     PISSN: 09295313     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10827-009-0148-4     Document Type: Article

References (32)

1. Birbaumer, N. (1999). Slow cortical potentials: Plasticity, operant control, and behavioral effects. The Neuroscientist, 5, 74-78. doi: 10.1177/107385849900500211.
2. Birbaumer, N., & Cohen, L. G. (2007). Brain-computer interfaces: Communication and restoration of movement in paralysis. The Journal of Physiology, 579, 621-636. doi: 10.1113/jphysiol.2006.125633.
3. Brener, J. (1981). Control of internal activities. British Medical Bulletin, 37, 169-174.
4. Brown, T. H., Ganong, A. H., Kairiss, E. W., Keenan, C. L., & Kelso, S. R. (1989). Long-term potentiation in two synaptic systems of the hippocampal brain slice. In J. H. Byrne & W. O. Berry (Eds.), Neural models of plasticity, pp. 266-306. San Diego: Academic Press.
5. Curran, E. A., & Stokes, M. J. (2003). Learning to Control Brain Activity: A review of the production and control of EEG components for driving brain-computer interface (BCI) systems. Brain and Cognition, 51, 326-336. doi: 10.1016/S0278-2626(03)00036-8.
6. Fabiani, G. E., McFarland, D. J., Wolpaw, J. R., & Pfurtscheller, G. (2000). Conversion of EEG activity into cursor movement by a brain-bomputer interface (BCI). IEEE Transactions on Neural Systems and Rehabilitation Engineering, 12(3), 331-338. doi: 10.1109/ TNSRE.2004.834627.
7. Fetz, E. E. (1969). Operant conditioning of cortical unit activity. Science, 163 (870), 955-958. doi: 10.1126/science.163.3870.955.
8. Goulden, C. H. (1959). Methods of Statistical Analysis (2nd ed.). New York: Wiley.
9. Hinterberger, T., Weiskopf, N., Veit, R., Wilhelm, B., Betta, E., & Birbaumer, N. (2004). An EEG-driven brain-computer interface combined with functional magnetic resonance imaging (fMRI). IEEE Transactions on Bio-Medical Engineering, 51, 971-974. doi: 10.1109/TBME.2004.827069.
10. Howe, R. C., & Sterman, M. B. (1972). Cortical-subcortical EEG correlates of suppressed motor behavior during sleep and waking in the cat. Electroencephalography and Clinical Neurophysiology, 32, 681-695. doi: 10.1016/0013-4694(72)90104-6.
11. Julien, K., Svyatoslav, V., & Thierry, P.(2005). Analysis of bit-rate definitions for brain-computer interfaces. International conference on Human-Computer Interaction (HCI'05), Las Vegas, Nevada, USA.
12. Kimmel, H. D. (1974). Instrumental conditioning of autonomically mediated responses in human beings. The American Psychologist, 29(5), 325-335. doi: 10.1037/h0037621.
13. Lopes da Silva, F. H., Hoeks, A., Smits, A., & Zetterberg, L. H. (1974). Model of brain rhythmic activity: The alpha-rhythm of the thalamus. Kybernetik, 15, 23-37. doi: 10.1007/BF00270757.
14. Lopes da Silva, F. H., Vos, J. E., Mooibroek, J., & Van Rotterdam, A. (1980). Relative contributions of intracortical and thalamo-cortical processes in the generation of alpha rhythms, revealed by partial coherence analysis. Electroencephalography and Clinical Neurophysiology, 50, 449-456. doi: 10.1016/0013-4694(80)90011-5.
15. Meinicke, P., Kaper, M., Hoppe, F., Heumann, M., & Ritter, H. (2003). Improving transfer rates in brain computer interface: A case study. Advances in Neural Information Processing Systems, 15, 1107-1114.
16. Miller, E. N. (1969). Learning of visceral and glandular responses. Science, 163, 434-445. doi: 10.1126/science.163.3866.434.
17. Nykopp, T. (2001). Statistical modeling issues for the adaptive brain interface. Master thesis, Helsinki University of Technology, Department of Electrical and Communication Engineering.
18. Obermaier, B., Neuper, C., Guger, C., & Pfurtscheller, G. (2001). Information transfer rate in a five-classes brain-computer interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 9 (3), 50-55. doi: 10.1109/7333.948456.
19. Obermaier, B., Müller, G. R., & Pfurtscheller, G. (2003). "Virtual Keyboard" controlled by spontaneous EEG activity. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 11(4), 422-426. doi: 10.1109/TNSRE.2003.816866.
20. Pfurtscheller, G., & Berghold, A. (1989). Patterns of cortical activation during planning of voluntary movement. Electroencephalography and Clinical Neurophysiology, 72, 250-258. doi: 10.1016/ 0013-4694(89)90250-2.
21. Pfurtscheller, G., & Lopes da Silva, F. H. (1999). Event-related EEG EMG synchronization and desynchronization: Basic principles. Clinical Neurophysiology, 110, 1842-1857. doi: 10.1016/S1388-2457(99)00141-8.
22. Pfurtscheller, G., & Neuper, C. (2001). Motor imagery and direct brain-computer communication. Proceedings of the IEEE, 89, 1123-1134. doi: 10.1109/5.939829.
23. Poulton, E. C. (1974). Sine wave tracks. In Tracking skill and manual control, pp. 107-139. New York: Academic Press.
24. Sellers, E. W., & Donchin, E. A. (2006). A P300 based brain computer interface: Initial tests by ALS patients. Clinical Neurophysiology, 117, 538-548. doi: 10.1016/j.clinph.2005.06.027.
25. Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27, 379-423.
26. Sterman, M. B. (1981). EEG biofeedback: Physiological behavior modification. Neuroscience and Biobehavioral Reviews, 5, 405-412. doi: 10.1016/0149-7634(81)90036-1.
27. Suffczynski, P., Pijn, J. P. M., Pfurtscheller, G., & Lopes da Silva, F. H. (1999). Event related dynamics of alpha band rhythms: A neuronal network model of focal ERD/surround ERS. In G. Pfurtscheller & F. H. Lopes da Silva (Eds.), Event-Related Desynchronization. (Volume 6, Handbook of Electroencephalography and Clinical Neurophysiology - Revised Series), pp. 67-85. New York: Elsevier.
28. Suffczynski, P., Kalitzin, S., Pfurtscheller, G., & Lopes da Silva, F. H. (2001). Computational model of thalamo-cortical networks: Dynamical control of alpha rhythms in relation to focal attention. International Journal of Psychophysiology, 43, 25-40. doi: 10.1016/ S0167-8760(01)00177-5.
29. Vaughan, T. M., Wolpaw, J. R., & Donchin, E. (1996). EEG-based communication: Prospects and problems. IEEE Transactions on Rehabilitation Engineering, 4, 425-430. doi: 10.1109/86.547945.
30. Wilson, H. R., & Cowan, J. D. (1972). Excitatory and inhibitory interactions in localized populations of model neurons. Biophysical Journal, 12, 1-25. doi: 10.1016/S0006-3495(72)86068-5.
31. Wright, J. J., Bourke, P. D., & Chapman, C. L. (2000). Synchronous oscillation in the cerebral cortex and object coherence: Simulation of basic electrophysiological findings. Biological Cybernetics, 83, 341-353. doi: 10.1007/s004220000155.
32. Wyrwicka, W., & Sterman, M. B. (1968). Instrumental conditioning of sensorimotor cortex EEG spindles in the waking cat. Physiology & Behavior, 3, 703-707. doi: 10.1016/0031-9384(68)90139-X.