Bayesian inference for an adaptive Ordered Probit model: An application to Brain Computer Interfacing

Neural Networks

Volumn 24, Issue 7, 2011, Pages 726-734

  Yoon, Ji Won ^a   Roberts, Stephen J ^a   Dyson, Mathew ^b   Gan, John Q ^b  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b UNIVERSITY OF ESSEX   (United Kingdom)

Author keywords

Brain Computer Interfacing; Extended Kalman Filter; Multi class classifier; Ordered Probit model; Sequential decisions

Indexed keywords

APPLICATION DOMAINS; BAYESIAN INFERENCE; BIOMEDICAL APPLICATIONS; BRAIN-COMPUTER INTERFACING; CLASSIFICATION APPROACH; DYNAMIC CLASSIFICATION; EEG SIGNALS; MULTI-CLASS CLASSIFIER; MULTIPLE CLASS; NOISY OBSERVATIONS; NONLINEAR AND NON-GAUSSIAN; ORDERED PROBIT MODEL; SENSOR NOISE; SEQUENTIAL DECISIONS; SYNTHETIC DATASETS;

ADAPTIVE ALGORITHMS; BAYESIAN NETWORKS; CLASSIFICATION (OF INFORMATION); COMPUTER APPLICATIONS; EXTENDED KALMAN FILTERS; INFERENCE ENGINES; MATHEMATICAL MODELS; MEDICAL APPLICATIONS; POSITION CONTROL;

BRAIN COMPUTER INTERFACE;

ANALYTICAL ERROR; ARTICLE; BAYESIAN LEARNING; BRAIN COMPUTER INTERFACE; CLASSIFICATION ALGORITHM; CONTROLLED STUDY; ELECTROENCEPHALOGRAM; HUMAN; HUMAN EXPERIMENT; INFORMATION PROCESSING; MATHEMATICAL MODEL; NONLINEAR SYSTEM; NORMAL HUMAN; ORDERED PROBIT MODEL; PREDICTION; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC;

ALGORITHMS; BAYES THEOREM; BRAIN; COMPUTER SIMULATION; ELECTROENCEPHALOGRAPHY; HUMANS; NONLINEAR DYNAMICS; SIGNAL PROCESSING, COMPUTER-ASSISTED; USER-COMPUTER INTERFACE;

EID: 79960890851     PISSN: 08936080     EISSN: 18792782     Source Type: Journal    
DOI: 10.1016/j.neunet.2011.03.019     Document Type: Article

References (30)

1. Aitchison J., Silvey S.D. The generalization of probit analysis to the case of multiple response. Biometrika 1957, 44:131-140.
2. Albert J.H., Chip S. Bayesian analysis of binary and polychotomous response data. Journal of American Statistical Association 1993, 88(422):669-679.
3. Babiloni F., Cincotti F., Lazzarini L., Millan J., Mourino J., Varsta M., et al. Linear classification of low-resolution EEG patterns produced by imagined hand movements. IEEE Transactions on Rehabilitation Engineering 2000, 8(2):186-188.
4. Bishop C.M. Pattern recognition and machine learning 1988, Springer. first ed.
5. Cameron A.C., Trivedi P.K. Regression analysis of count data 2006, Cambridge University Press. fifth ed.
6. Dornhege G., Blankertz B., Curio G., Muller K.-R. Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms. IEEE Transactions on Biomedical Engineering 2004, 51(6):993-1002.
7. Geweke J., Keane M., Runkle D. Alternative computational approaches to inference in the Multinomial Probit model. The Review of Economics and Statistics 1994, 76(4):609-632.
8. Haselsteiner E., Pfurtscheller G. Using time-dependent neural networks for EEG classification. IEEE Transactions on Rehabilitation Engineering 2000, 8(4):457-463.
9. Jung R.C., Kukuk M., Liesenfeld R. Time series of count data: modeling, estimation and diagnostics. Computational Statistics & Data Analysis 2006, 51:2350-2364.
10. Lotte F., Congedo M., Lécuyer A., Lamarche F., Arnaldi B. A review of classification algorithms for EEG-based brain-computer interfaces. Journal of Neural Engineering 2007, 4.
11. Lowne D.R., Roberts S.J., Garnett R. Sequential non-stationary dynamic classification with sparse feedback. Pattern Recognition 2010, 43(3):897-905.
12. Maddala G.S. Limited dependent and qualitative variables in econometrics 1983, Cambridge University Press.
13. McCulloch R., Rossi P.E. An exact likelihood analysis of the multinomial probit model. Journal of Econometrics 1994, 64(1-2):207-240.
14. Muller K.R., Anderson C.W., Birch G.E. Linear and nonlinear methods for brain-computer interfaces. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2003, 11(2):165-169.
15. Munkin M.K., Trivedi P.K. Bayesian analysis of the ordered probit model with endogenous selection. Journal of Econometrics 2007, 143:334-348.
16. Obermaier B., Guger C., Neuper C., Pfurtscheller G. Hidden Markov models for online classification of single trial EEG data. Pattern Recognition Letters 2001, 22(12):1299-1309.
17. Orguner U., Demrekler M. Analysis of single Gaussian approximation of Gaussian mixtures in Bayesian filtering applied to mixed multiple-model estimation. Internaltional Journal of Control 2007, 80(6):952-967.
18. Pardey J., Roberts S.J., Tarassenko L. A review of parametric modelling techniques for EEG analysis. Medical Engineering & Physics 1996, 18(1):2-11.
19. Penny W.D., Roberts S.J., Curran E.A., Stokes M. EEG-based communication: a pattern recognition approach. IEEE Transactions on Rehabilitation Engineering 2000, 8(2):214-216.
20. Pfurtscheller G., Flotzinger D., Kalcher J. Brain-computer interface: a new communication device for handicapped persons. Journal of Microcomputer Applications archive 1993, 16(3):293-299.
21. Ronning G., Kukuk M. Efficient estimation of ordered probit models. Journal of the American Statistical Association 1996, 91(435):1120-1129.
22. Sugiyama M., Krauledat M., Müller K. Covariate shift adaptation by importance weighted cross validation. Journal of Machine Learning Research 2007, 8:985-1005.
23. Sykacek P., Roberts S.J., Stokes M. Adaptive BCI based on variational Bayesian Kalman filtering: an empirical evaluation. IEEE Transactions on Biomedical Engineering 2004, 51(5):719-727.
24. Vidaurre C., Schlogl A., Cabeza R., Scherer R., Pfurtscheller G. A fully on-line adaptive BCI. IEEE Transactions on Biomedical Engineering 2006, 53:1214-1219.
25. Weiss A.A. Specification tests in ordered logit and probit models. Econometric Reviews 1997, 16:361-391.
26. Wolpaw J.R., McFarland D.J., Neat D.J., Forneris C.A. An EEG-based brain-computer interface for cursor control. Electroencephalography and Clinical Neurophysiology 1991, 78(3):252-259.
27. Yoon J., Roberts S.J., Dyson M., Gan J.Q. Adaptive classification by hybrid EKF with truncated filtering: brain computer interfacing. In International conference on intelligent data engineering and automated learning. Lecture notes in computer science 2008, Vol. 5326:370-377.
28. Yoon, J., Roberts, S. J., Dyson, M., & Gan, J. Q. (2008b). Sequential Bayesian estimation for adaptive classification. In IEEE international conference on multisensor fusion and integration for intelligent systems (pp. 601-605).
29. Yoon J., Roberts S.J., Dyson M., Gan J.Q. Adaptive classification for brain computer interface systems using sequential Monte Carlo sampling. Neural Networks 2009, 22:1286-1294.
30. Zhou X., Wang X., Dougherty E.R. Multi-class cancer classification using multinomial probit regression with Bayesian gene selection. IEE Proceedings on Systems Biology 2006, 153(2):70-78.