Ensemble classifier based on optimized extreme learning machine for motor imagery classification

Journal of Neural Engineering
Li ZhangRui Zhu

Abstract

Designing an effective classifier with high classification accuracy and strong generalization capability is essential for brain-computer interface (BCI) research. In this study, an extreme learning machine (ELM) based method is proposed to improve the classification accuracy of motor imagery electroencephalogram (EEG). The proposed method constructs an ensemble classifier based on optimized ELMs. Particle swarm optimization is used to simultaneously optimize the input weights and hidden biases of ELM to avoid the randomness and instability of classification result when ELM uses randomly generated parameters, and majority voting strategy is used to fuse the classification results of multiple base classifiers to avoid the negative impact of ELM with local optimal parameters on classification result. The proposed method was compared with four competing methods in experiments based on two public EEG datasets and some existing methods reported in the literature using the same datasets as well. The results indicate that the proposed method achieved significant higher classification accuracies than those of the competing methods on both two-class and four-class motor imagery data. Moreover, compared to the existing methods, it still o...Continue Reading

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Related Concepts

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Electroencephalogram
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Electroencephalography
Imagery
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