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A Method Based on Filter Bank Common Spatial Pattern for Multiclass Motor Imagery BCI

  • Ziqing Xia
  • Likun XiaEmail author
  • Ming Ma
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11872)

Abstract

The Common Spatial Pattern (CSP) algorithm is capable of solving the binary classification problem for the motor image task brain-computer interface (BCI). This paper proposes a novel method based on the Filter Bank Common Spatial Pattern (FBCSP) termed the Multiscale and Overlapping FBCSP (MO-FBCSP). We extend the CSP algorithm for multiclass by using the one-versus-one (OvO) strategy. Multiple periods are selected and combined with the overlapping spectrum of the filter bank which contains useful information. This method is evaluated on the benchmark BCI Competition IV dataset 2a with 9 subjects. An average accuracy of 80% was achieved with the random forest (RF) classifier, and the corresponding kappa value was 0.734. Quantitative results have shown that the proposed scheme outperforms the classical FBCSP algorithm by over 12%.

Keywords

Brain-computer interface Motor imagery Machine learning EEG 

Notes

Acknowledgement

This work was partially supported by a Natural Science Foundation of China (NSFC) grant (61572076) and Beijing Advanced Innovation Center for Imaging Technology.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.College of Information EngineeringCapital Normal UniversityBeijingChina
  2. 2.Laboratory of Neural Computing and Intelligent Perception (NCIP)New YorkUSA
  3. 3.International Science and Technology Cooperation Base of Electronic System Reliability and Mathematical InterdisciplinaryBeijingChina
  4. 4.Beijing Advanced Innovation Center for Imaging TechnologyBeijingChina
  5. 5.School of MedicineStanford UniversityStanfordUSA

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