Weitere Artikel dieser Ausgabe durch Wischen aufrufen
Action Editor: Steven J. Schiff
The primary goal of this study was to construct a simulation model of a biofeedback brain-computer interface (BCI) system to analyze the effect of biofeedback training on BCI users. A mathematical model of a man-machine visual-biofeedback BCI system was constructed to simulate a subject using a BCI system to control cursor movements. The model consisted of a visual tracking system, a thalamo-cortical model for EEG generation, and a BCI system. The BCI system in the model was realized for real experiments of visual biofeedback training. Ten sessions of visual biofeedback training were performed in eight normal subjects during a 3-week period. The task was to move a cursor horizontally across a screen, or to hold it at the screen’s center. Experimental conditions and EEG data obtained from real experiments were then simulated with the model. Three model parameters, representing the adaptation rate of gain in the visual tracking system and the relative synaptic strength between the thalamic reticular and thalamo-cortical cells in the Rolandic areas, were estimated by optimization techniques so that the performance of the model best fitted the experimental results. The serial changes of these parameters over the ten sessions, reflecting the effects of biofeedback training, were analyzed. The model simulation could reproduce results similar to the experimental data. The group mean success rate and information transfer rate improved significantly after training (56.6 to 81.1% and 0.19 to 0.76 bits/trial, respectively). All three model parameters displayed similar and statistically significant increasing trends with time. Extensive simulation with systematic changes of these parameters also demonstrated that assigning larger values to the parameters improved the BCI performance. We constructed a model of a biofeedback BCI system that could simulate experimental data and the effect of training. The simulation results implied that the improvement was achieved through a quicker adaptation rate in visual tracking gain and a larger synaptic gain from the visual tracking system to the thalamic reticular cells. In addition to the purpose of this study, the constructed biofeedback BCI model can also be used both to investigate the effects of different biofeedback paradigms and to test, estimate, or predict the performances of other newly developed BCI signal processing algorithms.
Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten
Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:
Brener, J. (1981). Control of internal activities. British Medical Bulletin, 37, 169–174. PubMed
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.
Goulden, C. H. (1959). Methods of Statistical Analysis (2nd ed.). New York: Wiley.
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.
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. CrossRefPubMed
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.
Nykopp, T. (2001). Statistical modeling issues for the adaptive brain interface. Master thesis, Helsinki University of Technology, Department of Electrical and Communication Engineering.
Poulton, E. C. (1974). Sine wave tracks. In Tracking skill and manual control, pp. 107–139. New York: Academic Press.
Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27, 379–423.
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.
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. CrossRefPubMed
- Model analyses of visual biofeedback training for EEG-based brain-computer interface
Chou-Ching K. Lin
- Springer US
Neuer Inhalt/© ITandMEDIA, Product Lifecycle Management/© Eisenhans | vege | Fotolia