Classification of EEG signals using the wavelet transform
Signal Processing
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
Feature extraction from Doppler ultrasound signals for automated diagnostic systems
Computers in Biology and Medicine
Recurrent neural networks employing Lyapunov exponents for EEG signals classification
Expert Systems with Applications: An International Journal
A log-linearized Gaussian mixture network and its application toEEG pattern classification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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
Learning vector quantization for the probabilistic neural network
IEEE Transactions on Neural Networks
Statistics over features: EEG signals analysis
Computers in Biology and Medicine
Expert Systems with Applications: An International Journal
Characterizing an unknown pollution source in groundwater resources systems using PSVM and PNN
Expert Systems with Applications: An International Journal
Enhanced probabilistic neural network with local decision circles: A robust classifier
Integrated Computer-Aided Engineering
Review article: Human scalp EEG processing: Various soft computing approaches
Applied Soft Computing
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A new approach based on the implementation of probabilistic neural network (PNN) 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. Because of the importance of making the right decision, the present work is carried out for searching better classification procedures for the EEG signals. 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 the PNN. 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 power levels of the power spectral density (PSD) estimates obtained by the eigenvector methods are the features which well represent the EEG signals and the PNN trained on these features achieved high classification accuracies.