Regularization and kernel parameters optimization based on PSO algorithm in EEG signals classification with SVM

Özbeyaz, Abdurrahman; Gürsoy, Mehmet İsmail; Çoban, Ramazan

doi:10.1109/siu.2011.5929671

Cited by 9 publications

(4 citation statements)

References 3 publications

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“…The parameters that directly affect the generalization ability of the classifier balance the relationship between minimizing the training error and maximizing the margin between classes (Bousseta et al, 2016). The most suitable hyperplane is found by adjusting the parameters c and g, which plays an essential role in the classification effect of the classifier (Liu et al, 2012;Özbeyaz et al, 2011). Here, satisfactory results are obtained by using an RBF kernel function defined by:…”

Section: Classification 241 Svm Classifiermentioning

confidence: 99%

Decoding spectro-temporal representation for motor imagery recognition using ECoG-based brain-computer interfaces

Zheng

Shan

et al. 2020

J. Integr. Neurosci.

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One of the challenges in brain-computer interface systems is obtaining motor imagery recognition from brain activities. Brain-signal decoding robustness and system performance improvement during the motor imagery process are two of the essential issues in brain-computer interface research. In conventional approaches, ineffective decoding of features and high complexity of algorithms often lead to unsatisfactory performance. A novel method for the recognition of motor imagery tasks is developed based on employing a modified S-transforms for spectro-temporal representation to characterize the behavior of electrocorticogram activities. A classifier is trained by using a support vector machine, and an optimized wrapper approach is applied to guide selection to implement the representation selection obtained. A channel selection algorithm optimizes the wrapper approach by adding a cross-validation step, which effectively improves the classification performance. The modified S-transform can accurately capture event-related desynchronization/event-related synchronization phenomena and can effectively locate sensorimotor rhythm information. The optimized wrapper approach used in this scheme can effectively reduce the feature dimension and improve algorithm efficiency. The method is evaluated on a public electrocorticogram dataset with a recognition accuracy of 98% and an information transfer rate of 0.8586 bit/trial. To verify the effect of the channel selection, both electrocorticogram and electroencephalogram data are experimentally analyzed. Furthermore, the computational efficiency of this scheme demonstrates its potential for online brain-computer interface systems in future cognitive tasks.

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Section: Classification 241 Svm Classifiermentioning

confidence: 99%

Decoding spectro-temporal representation for motor imagery recognition using ECoG-based brain-computer interfaces

Zheng

Shan

et al. 2020

J. Integr. Neurosci.

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“…The basic principle of Particle Swarm Optimization (PSO) [31][32][33] is as follows: suppose that an ethnic group = ( 1 , 2 , . .…”

Section: Parameters Optimization Based On Particle Swarm Algorithmmentioning

confidence: 99%

Road Surface State Recognition Based on SVM Optimization and Image Segmentation Processing

Zhao

Chen³

2017

Journal of Advanced Transportation

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Adverse road condition is the main cause of traffic accidents. Road surface condition recognition based on video image has become a central issue. However, hybrid road surface and road surface under different lighting environments are two crucial problems. In this paper, the road surface states are categorized into 5 types including dry, wet, snow, ice, and water. Then, according to the original image size, images are segmented; 9-dimensional color eigenvectors and 4 texture eigenvectors are extracted to construct road surface state characteristics database. Next, a recognition method of road surface state based on SVM (Support Vector Machine) is proposed. In order to improve the recognition accuracy and the universality, a grid searching algorithm and PSO (Particle Swarm Optimization) algorithm are used to optimize the kernel function factor and penalty factor of SVM. Finally, a large number of actual road surface images in different environments are tested. The results show that the method based on SVM and image segmentation is feasible. The accuracy of PSO algorithm is more than 90%, which effectively solves the problem of road surface state recognition under the condition of hybrid or different video scenes.

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“…To address this problem, they proposed a novel method, named binary multi-objective particle swarm optimization (BMOPSO) for channel reduction. In 2011, zbeyaz et al [163] used PSO for Regularization and Kernel Parameters Optimization in EEG Signals Classification with SVM. In this study, firstly power spectrum was obtained by applying Auto-Regressive Burg (ARBurg) method to the EEG signals.…”

Section: Pso In Eeg Signal Analysismentioning

confidence: 99%

Swarm Intelligence in Multiple and Many Objectives Optimization: A Survey and Topical Study on EEG Signal Analysis

Mishra

Dehuri

Cho

2015

Multi-Objective Swarm Intelligence

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This paper systematically presents the Swarm Intelligence (SI) methods for optimization of multiple and many objective problems. The fundamental difference of Multiple and Many Objective Optimization problems have been studied very rigorously. The three forefront swarm intelligence methods, i.e., Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Artificial Bee Colony Optimization (ABC) has been deeply studied to understand their ways of solving multiple and many objective problems distinctly. A pragmatic topical study on the behavior of real ants, bird flocks, and honey bees in solving EEG signal analysis completes the survey followed by discussion and extensive number of relevant references.

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Regularization and kernel parameters optimization based on PSO algorithm in EEG signals classification with SVM

Cited by 9 publications

References 3 publications

Decoding spectro-temporal representation for motor imagery recognition using ECoG-based brain-computer interfaces

Decoding spectro-temporal representation for motor imagery recognition using ECoG-based brain-computer interfaces

Road Surface State Recognition Based on SVM Optimization and Image Segmentation Processing

Swarm Intelligence in Multiple and Many Objectives Optimization: A Survey and Topical Study on EEG Signal Analysis

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