A novel channel selection method for optimal classification in different motor imagery BCI paradigms.
BACKGROUND
For sensorimotor rhythms based brain-computer interface (BCI) systems, classification of different motor imageries (MIs) remains a crucial problem. An important aspect is how many scalp electrodes (channels) should be used in order to reach optimal performance classifying motor imaginations. While the previous researches on channel selection mainly focus on MI tasks paradigms without feedback, the present work aims to investigate the optimal channel selection in MI tasks paradigms with real-time feedback (two-class control and four-class control paradigms).


METHODS
In the present study, three datasets respectively recorded from MI tasks experiment, two-class control and four-class control experiments were analyzed offline. Multiple frequency-spatial synthesized features were comprehensively extracted from every channel, and a new enhanced method IterRelCen was proposed to perform channel selection. IterRelCen was constructed based on Relief algorithm, but was enhanced from two aspects: change of target sample selection strategy and adoption of the idea of iterative computation, and thus performed more robust in feature selection. Finally, a multiclass support vector machine was applied as the classifier. The least number of channels that yield the best classification accuracy were considered as the optimal channels. One-way ANOVA was employed to test the significance of performance improvement among using optimal channels, all the channels and three typical MI channels (C3, C4, Cz).


RESULTS
The results show that the proposed method outperformed other channel selection methods by achieving average classification accuracies of 85.2, 94.1, and 83.2 % for the three datasets, respectively. Moreover, the channel selection results reveal that the average numbers of optimal channels were significantly different among the three MI paradigms.


CONCLUSIONS
It is demonstrated that IterRelCen has a strong ability for feature selection. In addition, the results have shown that the numbers of optimal channels in the three different motor imagery BCI paradigms are distinct. From a MI task paradigm, to a two-class control paradigm, and to a four-class control paradigm, the number of required channels for optimizing the classification accuracy increased. These findings may provide useful information to optimize EEG based BCI systems, and further improve the performance of noninvasive BCI.
College of Electrical Engineering, Zhejiang University, Hangzhou, 310027, China. workingshan@163.com.
Haijun
H
Shan
College of Electrical Engineering, Zhejiang University, Hangzhou, 310027, China. haojiexu@zju.edu.cn.
Haojie
H
Xu
College of Electrical Engineering, Zhejiang University, Hangzhou, 310027, China. zsa@zju.edu.cn.
Shanan
S
Zhu
Department of Biomedical Engineering and Institute for Engineering in Medicine, University of Minnesota, Minneapolis, MN, 55455, USA. binhe@umn.edu.
Bin
B
He
D000465:Algorithms; D062207:Brain-Computer Interfaces; D004569:Electroencephalography; D005246:Feedback; D007092:Imagination; D009043:Motor Activity