The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Digital signal processing (3rd ed.): principles, algorithms, and applications
Digital signal processing (3rd ed.): principles, algorithms, and applications
Classification of EEG signals using the wavelet transform
Signal Processing
Fuzzy least squares support vector machines for multiclass problems
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
Features extracted by eigenvector methods for detecting variability of EEG signals
Pattern Recognition Letters
Analysis of EEG signals by combining eigenvector methods and multiclass support vector machines
Computers in Biology and Medicine
Wavelet/mixture of experts network structure for EEG signals classification
Expert Systems with Applications: An International Journal
Decision support systems for time-varying biomedical signals: EEG signals classification
Expert Systems with Applications: An International Journal
Analysis of EEG signals by implementing eigenvector methods/recurrent neural networks
Digital Signal Processing
Recurrent neural networks employing Lyapunov exponents for EEG signals classification
Expert Systems with Applications: An International Journal
Multiclass Support Vector Machines for EEG-Signals Classification
IEEE Transactions on Information Technology in Biomedicine
Expert Systems with Applications: An International Journal
Fuzzy Hopfield neural network clustering for single-trial motor imagery EEG classification
Expert Systems with Applications: An International Journal
Clustering technique-based least square support vector machine for EEG signal classification
Computer Methods and Programs in Biomedicine
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
Identification of motor imagery tasks through CC-LR algorithm in brain computer interface
International Journal of Bioinformatics Research and Applications
Automated EEG analysis of epilepsy: A review
Knowledge-Based Systems
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The aim of the study is classification of the electroencephalogram (EEG) signals by combination of the model-based methods and the least squares support vector machines (LS-SVMs). The LS-SVMs were implemented for classification of two types of EEG signals (set A - EEG signals recorded from healthy volunteers with eyes open and set E - EEG signals recorded from epilepsy patients during epileptic seizures). In order to extract the features representing the EEG signals, the spectral analysis of the EEG signals was performed by using the three model-based methods (Burg autoregressive - AR, moving average - MA, least squares modified Yule-Walker autoregressive moving average - ARMA methods). The present research demonstrated that the Burg AR coefficients are the features which well represent the EEG signals and the LS-SVM trained on these features achieved high classification accuracies.