EEG Signals Classification Using a Hybrid Method Based on Negative Selection and Particle Swarm Optimization

Ba-Karait, Nasser Omer Sahel; Shamsuddin, Siti Mariyam; Sudirman, Rubita

doi:10.1007/978-3-642-31537-4_34

Cited by 9 publications

(1 citation statement)

References 24 publications

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“…PSO-based RBFNN [30] and PSO-based recurrent NN [31] are examples of these hybrid dynamic classifiers. Karait, Shamsuddin and Sudirman [32] introduced a hybrid PSO called adaptive particle swarm negative selection (APSNS) for EEG signal classification. It is noteworthy that as far as the authors' knowledge is concerned, quantum-behaved PSO has only been applied to EEG feature selection task [33,34].…”

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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