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

Hassani, Kaveh; Lee, Won-Sook

doi:10.1109/civemsa.2014.6841436

Cited by 13 publications

(6 citation statements)

References 39 publications

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“…They compared the performance of IQPSO against ten other classifiers on two EEG datasets. The results suggested that IQPSO outperformed other classifiers in terms of classification accuracy, precision and recall [18].…”

Section: A the Classification Studies On Eeg Eye State Medical Datasetmentioning

confidence: 88%

Performance of Classification Techniques on Medical Datasets

Tutuncu¹,

Köklü²

2015

Third International Conference on Advances in Bio-Informatics and Environmental Engineering - ICABEE 2015

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Section: A the Classification Studies On Eeg Eye State Medical Datasetmentioning

confidence: 88%

Performance of Classification Techniques on Medical Datasets

Tutuncu¹,

Köklü²

2015

Third International Conference on Advances in Bio-Informatics and Environmental Engineering - ICABEE 2015

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“…Other algorithms like Ant Colony Optimization (ACO) [34] or Particle Swarm Optimization (PSO) [35] have also been used. Adaptive approaches have been employed as well, like the auto-reinforced system introduced in [36], by incorporating a fed-back PSO and a rule-based classifier.…”

Section: Related Workmentioning

confidence: 99%

EEG Feature Extraction Using Genetic Programming for the Classification of Mental States

et al. 2020

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The design of efficient electroencephalogram (EEG) classification systems for the detection of mental states is still an open problem. Such systems can be used to provide assistance to humans in tasks where a certain level of alertness is required, like in surgery or in the operation of heavy machines, among others. In this work, we extend a previous study where a classification system is proposed using a Common Spatial Pattern (CSP) and Linear Discriminant Analysis (LDA) for the classification of two mental states, namely a relaxed and a normal state. Here, we propose an enhanced feature extraction algorithm (Augmented Feature Extraction with Genetic Programming, or +FEGP) that improves upon previous results by employing a Genetic-Programming-based methodology on top of the CSP. The proposed algorithm searches for non-linear transformations that build new features and simplify the classification task. Although the proposed algorithm can be coupled with any classifier, LDA achieves 78.8% accuracy, the best predictive accuracy among tested classifiers, significantly improving upon previously published results on the same real-world dataset.

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“…Classes with more training objects will have a higher tendency to influence the overall prediction. Thus, instead of using the FIFO approach, it is recommended to identify and store only the significant objects while eliminating the rarely used objects [16] for biometric authentication modelling.…”

Section: Literature Reviewmentioning

confidence: 99%

EEG‐based biometric authentication modelling using incremental fuzzy‐rough nearest neighbour technique

et al. 2017

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This paper proposes an Incremental Fuzzy-Rough Nearest Neighbour (IncFRNN) technique for biometric authentication modelling using feature extracted visual evoked. Only small training set is needed for model initialisation. The embedded heuristic update method adjusts the knowledge granules incrementally to maintain all representative electroencephalogram (EEG) signal patterns and eliminate those rarely used. It reshapes the personalized knowledge granules through insertion and deletion of a test object, based on similarity measures. A predefined window size can be used to reduce the overall processing time. This proposed algorithm was verified with test data from 37 healthy subjects. Signal pre-processing steps on segmentation, filtering and artefact rejection were carried out to improve the data quality before model building. The experimental paradigm was designed in three different conditions to evaluate the authentication performance of the IncFRNN technique against the benchmarked incremental K-Nearest Neighbour (KNN) technique. The performance was measured in terms of accuracy, area under the Receiver Operating Characteristic (ROC) curve (AUC) and Cohen's Kappa coefficient. The proposed IncFRNN technique is proven to be statistically better than the KNN technique in the controlled window size environment. Future work will focus on the use of dynamic data features to improve the robustness of the proposed model.

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

Cited by 13 publications

References 39 publications

Performance of Classification Techniques on Medical Datasets

Performance of Classification Techniques on Medical Datasets

EEG Feature Extraction Using Genetic Programming for the Classification of Mental States

EEG‐based biometric authentication modelling using incremental fuzzy‐rough nearest neighbour technique

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