An Efficient Framework for EEG Analysis with Application to Hybrid Brain Computer Interfaces Based on Motor Imagery and P300.
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.
College of Information Science and Technology, Jinan University, Guangzhou 510632, China; School of Automation Science and Engineering, South China University of Technology and Guangzhou Key Laboratory of Brain Computer Interaction and Applications, Guangzhou 510640, China; Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China.
Jinyi
0000-0001-6150-987X
J
Long
School of Automation Science and Engineering, South China University of Technology and Guangzhou Key Laboratory of Brain Computer Interaction and Applications, Guangzhou 510640, China.
Jue
J
Wang
School of Automation Science and Engineering, South China University of Technology and Guangzhou Key Laboratory of Brain Computer Interaction and Applications, Guangzhou 510640, China.
Tianyou
T
Yu
D000328:Adult; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping; D062207:Brain-Computer Interfaces; D016002:Discriminant Analysis; D004569:Electroencephalography; D018913:Event-Related Potentials, P300 / Q000502:physiology*; D005260:Female; D006801:Humans; D007092:Imagination; D008297:Male; D008875:Middle Aged; D009068:Movement / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted; D055815:Young Adult