Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

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

  • Published:
Journal of Computational Neuroscience Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

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

References

  • Birbaumer, N. (1999). Slow cortical potentials: Plasticity, operant control, and behavioral effects. The Neuroscientist, 5, 74–78. doi:10.1177/107385849900500211.

    Article  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • Brener, J. (1981). Control of internal activities. British Medical Bulletin, 37, 169–174.

    CAS  PubMed  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Fetz, E. E. (1969). Operant conditioning of cortical unit activity. Science, 163(870), 955–958. doi:10.1126/science.163.3870.955.

    Article  CAS  PubMed  Google Scholar 

  • Goulden, C. H. (1959). Methods of Statistical Analysis (2nd ed.). New York: Wiley.

    Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

  • Kimmel, H. D. (1974). Instrumental conditioning of autonomically mediated responses in human beings. The American Psychologist, 29(5), 325–335. doi:10.1037/h0037621.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Google Scholar 

  • Miller, E. N. (1969). Learning of visceral and glandular responses. Science, 163, 434–445. doi:10.1126/science.163.3866.434.

    Article  CAS  PubMed  Google Scholar 

  • Nykopp, T. (2001). Statistical modeling issues for the adaptive brain interface. Master thesis, Helsinki University of Technology, Department of Electrical and Communication Engineering.

  • 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.

    Article  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • Pfurtscheller, G., & Neuper, C. (2001). Motor imagery and direct brain-computer communication. Proceedings of the IEEE, 89, 1123–1134. doi:10.1109/5.939829.

    Article  Google Scholar 

  • Poulton, E. C. (1974). Sine wave tracks. In Tracking skill and manual control, pp. 107–139. New York: Academic Press.

    Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27, 379–423.

    Google Scholar 

  • Sterman, M. B. (1981). EEG biofeedback: Physiological behavior modification. Neuroscience and Biobehavioral Reviews, 5, 405–412. doi:10.1016/0149-7634(81)90036-1.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  Google Scholar 

Download references

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.

Author information

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

Authors
  1. Chih-Wei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ming-Shaung Ju
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yun-Nien Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chou-Ching K. Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chou-Ching K. Lin.

Additional information

Action Editor: Steven J. Schiff

Rights and permissions

Reprints and permissions

About this article

Cite this article

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

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

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

Keywords

Search

Navigation