IMR Press / JIN / Volume 18 / Issue 2 / DOI: 10.31083/j.jin.2019.02.17
Open Access Original Research
Channel selection in motor imaginary-based brain-computer interfaces: a particle swarm optimization algorithm
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1 Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, P.R. China
*Correspondence: (Qingguo Wei)
J. Integr. Neurosci. 2019, 18(2), 141–152;
Submitted: 17 December 2018 | Accepted: 12 April 2019 | Published: 30 June 2019
Copyright: © 2019 Zhang and Wei Published by IMR press.
This is an open access article under the CC BY-NC 4.0 license (

The number of electrode channels in a brain-computer interface affects not only its classification performance, but also its convenience in practical applications. However, an effective method for determining the number of channels has not yet been established for motor imagery-based brain-computer interfaces. This paper proposes a novel evolutionary search algorithm, binary quantum-behaved particle swarm optimization, for channel selection, which is implemented in a wrapping manner, coupling common spatial pattern for feature extraction, and support vector machine for classification. The fitness function of binary quantum-behaved particle swarm optimization is defined as the weighted sum of classification error rate and relative number of channels. The classification performance of the binary quantum-behaved particle swarm optimization-based common spatial pattern was evaluated on an electroencephalograph data set and an electrocorticography data set. It was subsequently compared with that of other three common spatial pattern methods: using the channels selected by binary particle swarm optimization, all channels in raw data sets, and channels selected manually. Experimental results showed that the proposed binary quantum-behaved particle swarm optimization-based common spatial pattern method outperformed the other three common spatial pattern methods, significantly decreasing the classification error rate and number of channels, as compared to the common spatial pattern method using whole channels in raw data sets. The proposed method can significantly improve the practicability and convenience of a motor imagery-based brain-computer interface system.

Brain-computer interface
motor imagery
common spatial pattern
channel selection
binary quantum-behaved particle swarm optimization
Figure 1.
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