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

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Abstract

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.

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Abbreviations

τ:

Time constant of visual tracking system

a1, a2:

Constants in linearized thalamo-cortical model

D:

Logical operator for assigning m to F1(s) or F2(s)

e:

Error (difference) between u and y, e = u-z

F1(s), F2(s):

Thalamo-cortical model for C3 and C4 positions, respectively.

\( {\text{f}}_{\text{C3}}^{\text{n}} \), \( {\text{f}}_{\text{C4}}^{\text{n}} \) :

power spectrum of the nth windowed y1 and y2, respectively

g1, g2 :

Gain magnifying m into thalamic reticular model of C3 and C4 positions, respectively

He, Hi :

Transfer function of excitatory and inhibitory post-synaptic potential, respectively

HRe, HRi :

Corresponding Transfer functions for reticular cells

HTe, HTi :

Corresponding Transfer functions for thalamo-cortical cells

J:

Cost function in optimization of estimating model parameters

k:

Proportional gain of visual tracking system

m:

Output of visual tracking system

m1, m2 :

Modulating input to thalamo-cortical model of C3 and C4 positions, respectively

N:

Number of targets

q:

Stationary random sensory input to thalamo-cortical model

Pc3, Pc4 :

Power ratio of mu rhythm of y1 and y2, respectively

Pc3*, Pc4*:

Power ratio of mu rhythm of y1* and y2*, respectively

QC3, QC4 :

Summed power in the 8–14 Hz band of y1 and y2, respectively

QT3, QT4 :

Summed power of y1 and y2, respectively

r:

Adaptation rate of tuning k.

RT, RR, RL, RC :

Success rate for total, moving rightward, moving leftward and holding central imaginary tasks, respectively.

S:

Setting upper and lower limits to BCI output

u:

Desired target of biofeedback training

V:

Visual tracking system

w:

A weighting constant in computing BCI output

x, i, j, n:

Dummy indices

y1, y2 :

Simulated EEG of the modified thalamo-cortical model at C3 and C4, respectively

y1*, y2*:

EEG at C3 and C4 obtained from real experiments, respectively

z:

Output of BCI model

z*:

Cursor movement recorded in experiments

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Acknowledgements

This research was partially supported by the ROC Department of Economics via a contract 96-EC-17-A-19-S1-053. We thank Dr. P. Suffczynski for generously offering the code of his thalamo-cortical model.

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Authors and Affiliations

  1. Department of Mechanical Engineering, National Cheng Kung University, Tainan, Taiwan

    Chih-Wei Chen & Ming-Shaung Ju

  2. Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan

    Yun-Nien Sun

  3. Department of Neurology, National Cheng Kung University Hospital, Tainan, Taiwan

    Chou-Ching K. Lin

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  1. Chih-Wei Chen
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  2. Ming-Shaung Ju
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Correspondence to Chou-Ching K. Lin.

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Chen, CW., Ju, MS., Sun, YN. et al. Model analyses of visual biofeedback training for EEG-based brain-computer interface. J Comput Neurosci 27, 357–368 (2009). https://doi.org/10.1007/s10827-009-0148-4

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  • DOI: https://doi.org/10.1007/s10827-009-0148-4

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