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Classification of motor imagery tasks for electrocorticogram based brain-computer interface

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Abstract

Purpose

In the present study, we propose a novel scheme for motor imagery (MI) classification of multichannel electrocorticogram (ECoG) recordings from patients with medically intractable focal epilepsy.

Methods

This scheme proposes a combination of the two features which includes autoregressive (AR) model coefficients and local binary pattern (LBP) operators. It can provide spatial resolution and angular space information. Then the gradient boosting (GB) in conjunction with ordinary least squares (OLS) algorithm is employed as the classifier to improve the performance of MI classification for ECoG based Brain Computer Interface (BCI) system.

Results

Experimental results on the BCI Competition III data set I indicate that the novel method has excellent performance and yields a cross-validation accuracy of 88.8% and accuracy of 93%, respectively.

Conclusions

From the experimental results and comparative studies, we can infer that the scheme may serve as a good MI classification tool for a better tradeoff between the classification accuracy and computational complexity.

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Authors and Affiliations

  1. School of Information Science and Engineering, Shandong University, Shandong, China

    Fangzhou Xu, Weidong Zhou & Qi Yuan

  2. Troops 72465 of PLA, Shandong, China

    Yilin Zhen

Authors
  1. Fangzhou Xu
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  2. Weidong Zhou
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  3. Yilin Zhen
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  4. Qi Yuan
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Correspondence to Weidong Zhou.

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Xu, F., Zhou, W., Zhen, Y. et al. Classification of motor imagery tasks for electrocorticogram based brain-computer interface. Biomed. Eng. Lett. 4, 149–157 (2014). https://doi.org/10.1007/s13534-014-0128-0

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  • DOI: https://doi.org/10.1007/s13534-014-0128-0

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