Least squares support vector machine employing model-based methods coefficients for analysis of EEG signals
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 YuleWalker 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.
least squares support vector machines model-based methods electroencephalogram (eeg) signals