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

https://doi.org/10.1155/2007/49037

Received: 21 October 2005

Accepted: 22 June 2006

Published: 19 October 2006

Abstract

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.

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Authors’ Affiliations

(1)
Department of Electronic Science and Technology, University of Science and Technology of China
(2)
Institute for Infocomm Research

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Copyright

© Zhu et al. 2007