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Computational Intelligence and Neuroscience
Volume 2017, Article ID 9528097, 6 pages
https://doi.org/10.1155/2017/9528097
Research Article

An Efficient Framework for EEG Analysis with Application to Hybrid Brain Computer Interfaces Based on Motor Imagery and P300

1College of Information Science and Technology, Jinan University, Guangzhou 510632, China
2School of Automation Science and Engineering, South China University of Technology and Guangzhou Key Laboratory of Brain Computer Interaction and Applications, Guangzhou 510640, China
3Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

Correspondence should be addressed to Jinyi Long; moc.liamg@iynijgnol

Received 21 August 2016; Revised 8 November 2016; Accepted 12 December 2016; Published 19 February 2017

Academic Editor: Feng Duan

Copyright © 2017 Jinyi Long 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.

Abstract

The hybrid brain computer interface (BCI) based on motor imagery (MI) and P300 has been a preferred strategy aiming to improve the detection performance through combining the features of each. However, current methods used for combining these two modalities optimize them separately, which does not result in optimal performance. Here, we present an efficient framework to optimize them together by concatenating the features of MI and P300 in a block diagonal form. Then a linear classifier under a dual spectral norm regularizer is applied to the combined features. Under this framework, the hybrid features of MI and P300 can be learned, selected, and combined together directly. Experimental results on the data set of hybrid BCI based on MI and P300 are provided to illustrate competitive performance of the proposed method against other conventional methods. This provides an evidence that the method used here contributes to the discrimination performance of the brain state in hybrid BCI.