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Chih-Wei Chen Ming-Shaung Ju Yun-Nien Sun Chou-Ching K. Lin 《Journal of computational neuroscience》2009,27(3):357-368
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. 相似文献
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