The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Classification of EEG signals using the wavelet transform
Signal Processing
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Recurrent neural networks employing Lyapunov exponents for analysis of doppler ultrasound signals
Expert Systems with Applications: An International Journal
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Feature extraction from Doppler ultrasound signals for automated diagnostic systems
Computers in Biology and Medicine
A novel large-memory neural network as an aid in medical diagnosis applications
IEEE Transactions on Information Technology in Biomedicine
Input feature selection for classification problems
IEEE Transactions on Neural Networks
Features for analysis of electrocardiographic changes in partial epileptic patients
Expert Systems with Applications: An International Journal
Combined neural network model employing wavelet coefficients for EEG signals classification
Digital Signal Processing
Statistics over features: EEG signals analysis
Computers in Biology and Medicine
Expert Systems with Applications: An International Journal
Lyapunov exponents/probabilistic neural networks for analysis of EEG signals
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A learning method for the class imbalance problem with medical data sets
Computers in Biology and Medicine
Computers in Biology and Medicine
EEG signal classification using PCA, ICA, LDA and support vector machines
Expert Systems with Applications: An International Journal
Journal of Medical Systems
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Time-frequency distributions in the classification of epilepsy from EEG signals
Expert Systems with Applications: An International Journal
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A new approach based on the implementation of multiclass support vector machine (SVM) with the error correcting output codes (ECOC) is presented for classification of electroencephalogram (EEG) signals. In practical applications of pattern recognition, there are often diverse features extracted from raw data which needs recognizing. Decision making was performed in two stages: feature extraction by eigenvector methods and classification using the classifiers trained on the extracted features. The aim of the study is classification of the EEG signals by the combination of eigenvector methods and multiclass SVM. The purpose is to determine an optimum classification scheme for this problem and also to infer clues about the extracted features. The present research demonstrated that the eigenvector methods are the features which well represent the EEG signals and the multiclass SVM trained on these features achieved high classification accuracies.