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Motor imagery classification based on joint regression model and spectral power

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Abstract

A brain–computer interface (BCI) based on motor imagery (MI) translates the subject’s motor intention into a control signal through classifying electroencephalogram (EEG) patterns of different imagination tasks, for example, hand movements. Auto-regression (AR) model is one of the popular methods to describe motor imagery patterns, which is widely used by researchers to resolve subject’s motor intention. In this paper, we use joint regression (JR) model and propose an algorithm by combining the coefficients of JR model and spectral powers at two specific frequencies to classify different MI patterns. The algorithm produces a classification accuracy of 90 % on the training data of one subject from BCI2003 Data set III and 80 % on the test data. The results are better than that by using AR model. We also apply the algorithm to MI tasks of one subject in our laboratory, and the classification accuracy can reach 97.86 % on the test data. The results demonstrate that the combination of JR model and spectral powers can achieve much higher accuracy for classification of MI tasks.

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Acknowledgments

This work is partially supported by Natural Science Foundation of China (No. 61070127), Qianjiang Talent Plan of Zhejiang Province, China (No. 2011R10063), U.S. National Science Foundation (NSF) under Grant 0821820, and Tennessee Higher Education Commission, the State of Tennessee, USA.

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Authors and Affiliations

  1. College of Computer Science, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, People’s Republic of China

    Sanqing Hu, Qiangqiang Tian, Jianhai Zhang & Wanzeng Kong

  2. College of Engineering and Computer Science, The University of Tennessee at Chattanooga, Chattanooga, TN, 37403, USA

    Yu Cao

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  1. Sanqing Hu
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  2. Qiangqiang Tian
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  3. Yu Cao
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  4. Jianhai Zhang
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Correspondence to Sanqing Hu.

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Hu, S., Tian, Q., Cao, Y. et al. Motor imagery classification based on joint regression model and spectral power. Neural Comput & Applic 23, 1931–1936 (2013). https://doi.org/10.1007/s00521-012-1244-3

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  • DOI: https://doi.org/10.1007/s00521-012-1244-3

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