Channel selection in motor imaginary-based brain-computer interfaces: a particle swarm optimization algorithm.

Channel selection in motor imaginary-based brain-computer interfaces: a particle swarm optimization algorithm. J Integr Neurosci. 2019 Jun 30;18(2):141-152 Authors: Zhang L, Wei Q Abstract 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 imagerybased brain-computer interfaces. This paper proposes a novel evolutionary search algorithm, binary quantumbehaved 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 opt...
Source: Journal of Integrative Neuroscience - Category: Neuroscience Authors: Tags: J Integr Neurosci Source Type: research