Towards Adaptive Classification of Motor Imagery EEG Using Biomimetic Pattern Recognition

Ge, Yanbin; Wu, Yan

doi:10.1007/978-3-642-25944-9_59

Cited by 1 publication

(1 citation statement)

References 9 publications

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“…Among them, incremental SVM is widely applied [10][11][12]. In some studies, incremental versions of biomimetic pattern recognition (BPR) approach are utilized for online EEG classification task [13][14][15]. Kai [13] concluded that incremental semi-supervised BPR demonstrates higher accuracy and stability than incremental SVM technique.…”

Section: Related Workmentioning

confidence: 99%

An incremental framework for classification of EEG signals using quantum particle swarm optimization

Hassani

Lee

2014

2014 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applicati

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Classification of electroencephalographic (EEG) signals is a sophisticated task that determines the accuracy of thought pattern recognition performed by computer-brain interface (BCI) which, in turn, determines the degree of naturalness of the interaction provided by that system. However, classifying the EEG signals is not a trivial task due to their nonstationary characteristics. In this paper, we introduce and utilize incremental quantum particle swarm optimization (IQPSO) algorithm for incremental classification of EEG data stream. IQPSO builds the classification model as a set of explicit rules which benefits from semantic symbolic knowledge representation and enhanced comprehensibility. We compared the performance of IQPSO against ten other classifiers on two EEG datasets. The results suggest that IQPSO outperforms other classifiers in terms of classification accuracy, precision and recall.

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