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BioMed Research International
Volume 2014, Article ID 159486, 13 pages
Research Article

Nonlinear EEG Decoding Based on a Particle Filter Model

Xi’an Jiaotong University, Qujiang Campus, West Building No. 5, No. 99 YanXiang Road, YanTa District, Xi’an, Shaanxi 710045, China

Received 28 February 2014; Revised 16 April 2014; Accepted 18 April 2014; Published 15 May 2014

Academic Editor: Ting Zhao

Copyright © 2014 Jinhua Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


While the world is stepping into the aging society, rehabilitation robots play a more and more important role in terms of both rehabilitation treatment and nursing of the patients with neurological diseases. Benefiting from the abundant contents of movement information, electroencephalography (EEG) has become a promising information source for rehabilitation robots control. Although the multiple linear regression model was used as the decoding model of EEG signals in some researches, it has been considered that it cannot reflect the nonlinear components of EEG signals. In order to overcome this shortcoming, we propose a nonlinear decoding model, the particle filter model. Two- and three-dimensional decoding experiments were performed to test the validity of this model. In decoding accuracy, the results are comparable to those of the multiple linear regression model and previous EEG studies. In addition, the particle filter model uses less training data and more frequency information than the multiple linear regression model, which shows the potential of nonlinear decoding models. Overall, the findings hold promise for the furtherance of EEG-based rehabilitation robots.