Skip to content


  • Research Article
  • Open Access

Expectation-Maximization Method for EEG-Based Continuous Cursor Control

  • Xiaoyuan Zhu1Email author,
  • Cuntai Guan2,
  • Jiankang Wu2,
  • Yimin Cheng1 and
  • Yixiao Wang1
EURASIP Journal on Advances in Signal Processing20062007:049037

Received: 21 October 2005

Accepted: 22 June 2006

Published: 19 October 2006


To develop effective learning algorithms for continuous prediction of cursor movement using EEG signals is a challenging research issue in brain-computer interface (BCI). In this paper, we propose a novel statistical approach based on expectation-maximization (EM) method to learn the parameters of a classifier for EEG-based cursor control. To train a classifier for continuous prediction, trials in training data-set are first divided into segments. The difficulty is that the actual intention (label) at each time interval (segment) is unknown. To handle the uncertainty of the segment label, we treat the unknown labels as the hidden variables in the lower bound on the log posterior and maximize this lower bound via an EM-like algorithm. Experimental results have shown that the averaged accuracy of the proposed method is among the best.


Learning AlgorithmQuantum InformationResearch IssueHide VariableChallenging Research


Authors’ Affiliations

Department of Electronic Science and Technology, University of Science and Technology of China, Anhui, China
Institute for Infocomm Research, Singapore


  1. Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM: Brain-computer interfaces for communication and control. Clinical Neurophysiology 2002,113(6):767-791. 10.1016/S1388-2457(02)00057-3View ArticleGoogle Scholar
  2. Birbaumer N, Hinterberger T, Kübler A, Neumann N: The thought-translation device (TTD): neurobehavioral mechanisms and clinical outcome. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2003,11(2):120-123. 10.1109/TNSRE.2003.814439View ArticleGoogle Scholar
  3. Pfurtscheller G, Neuper C: Motor imagery and direct brain-computer communication. Proceedings of the IEEE 2001,89(7):1123-1134. 10.1109/5.939829View ArticleGoogle Scholar
  4. Farwell LA, Donchin E: Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalography and Clinical Neurophysiology 1988,70(6):510-523. 10.1016/0013-4694(88)90149-6View ArticleGoogle Scholar
  5. Meinicke P, Kaper M, Hoppe F, Heumann M, Ritter H: Improving transfer rates in brain computer interfacing: a case study. In Advances in Neural Information Processing Systems. MIT Press, Cambridge, Mass, USA; 2003:1107-1114.Google Scholar
  6. Middendorf M, McMillan G, Calhoun G, Jones KS: Brain-computer interfaces based on the steady-state visual-evoked response. IEEE Transactions on Rehabilitation Engineering 2000,8(2):211-214. 10.1109/86.847819View ArticleGoogle Scholar
  7. Wolpaw JR, McFarland DJ, Vaughan TM, Schalk G: The Wadsworth Center brain-computer interface (BCI) research and development program. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2003,11(2):204-207. 10.1109/TNSRE.2003.814442View ArticleGoogle Scholar
  8. Wolpaw JR, McFarland DJ: Multichannel EEG-based brain-computer communication. Electroencephalography and Clinical Neurophysiology 1994,90(6):444-449. 10.1016/0013-4694(94)90135-XView ArticleGoogle Scholar
  9. McFarland DJ, Wolpaw JR: EEG-based communication and control: speed-accuracy relationships. Applied Psychophysiology Biofeedback 2003,28(3):217-231. 10.1023/A:1024685214655View ArticleGoogle Scholar
  10. Roberts SJ, Penny WD: Real-time brain-computer interfacing: a preliminary study using Bayesian learning. Medical and Biological Engineering and Computing 2000,38(1):56-61. 10.1007/BF02344689View ArticleGoogle Scholar
  11. Cheng M, Jia W, Gao X, Gao S, Yang F: Mu rhythm-based cursor control: an offline analysis. Clinical Neurophysiology 2004,115(4):745-751. 10.1016/j.clinph.2003.11.038View ArticleGoogle Scholar
  12. Blanchard G, Blankertz B: BCI competition 2003-data set IIa: spatial patterns of self-controlled brain rhythm modulations. IEEE Transactions on Biomedical Engineering 2004,51(6):1062-1066. 10.1109/TBME.2004.826691View ArticleGoogle Scholar
  13. Blankertz B, Müller K-R, Curio G, et al.: The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials. IEEE Transactions on Biomedical Engineering 2004,51(6):1044-1051. 10.1109/TBME.2004.826692View ArticleGoogle Scholar
  14. Dempster AP, Laird NM, Rubin DB: Maximum likelihood for incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B 1977, 39: 1-38.MathSciNetMATHGoogle Scholar
  15. Neal RM, Hinton GE: A view of the EM algorithm that justifies incremental, sparse, and other variants. In Learning in Graphical Models. Edited by: Jordan MI. Kluwer Academic, Dordrecht, The Netherlands; 1998:355-368.View ArticleGoogle Scholar
  16. Jordan MI, Ghahramani Z, Jaakkola TS, Saul LK: An introduction to variational methods for graphical models. In Learning in Graphical Models. Edited by: Jordan MI. MIT Press, Cambridge, Mass, USA; 1999.Google Scholar
  17. Bishop CM: Neural Networks for Pattern Recognition. Oxford University Press, Oxford, UK; 1995.MATHGoogle Scholar
  18. Jebara T: Machine Learning: Discriminative and Generative. Kluwer Academic, Dordrecht, The Netherlands; 2004.View ArticleMATHGoogle Scholar
  19. MacKay DJC: Bayesian interpolation. Neural Computation 1992,4(3):415-447. 10.1162/neco.1992.4.3.415View ArticleMATHGoogle Scholar
  20. Ramoser H, Müller-Gerking J, Pfurtscheller G: Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Transactions on Rehabilitation Engineering 2000,8(4):441-446. 10.1109/86.895946View ArticleGoogle Scholar
  21. MacKay DJC: The evidence framework applied to classification networks. Neural Computation 1992,4(5):698-714.View ArticleGoogle Scholar


© Zhu et al. 2007