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Expectation-Maximization Method for EEG-Based Continuous Cursor Control

EURASIP Journal on Advances in Signal Processing volume 2007, Article number: 049037 (2006) Cite this article

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 and Affiliations

  1. Department of Electronic Science and Technology, University of Science and Technology of China, Anhui, Hefei, 230027, China

    Xiaoyuan Zhu, Yimin Cheng & Yixiao Wang

  2. Institute for Infocomm Research, 21 Heng Mui Keng Terrace, 119613, Singapore

    Cuntai Guan & Jiankang Wu

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  1. Xiaoyuan Zhu
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  2. Cuntai Guan
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  3. Jiankang Wu
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  4. Yimin Cheng
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  5. Yixiao Wang
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Correspondence to Xiaoyuan Zhu.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Zhu, X., Guan, C., Wu, J. et al. Expectation-Maximization Method for EEG-Based Continuous Cursor Control. EURASIP J. Adv. Signal Process. 2007, 049037 (2006). https://doi.org/10.1155/2007/49037

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

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