Detection of seizure activity in EEG by an artificial neural network: a preliminary study
Computers and Biomedical Research
Neuro-fuzzy architectures and hybrid learning
Neuro-fuzzy architectures and hybrid learning
Foundations of Neuro-Fuzzy Systems
Foundations of Neuro-Fuzzy Systems
Fuzzy and Neuro-Fuzzy Systems in Medicine
Fuzzy and Neuro-Fuzzy Systems in Medicine
Comparison of Wavelet Transform and FFT Methods in the Analysis of EEG Signals
Journal of Medical Systems
EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks
JVA '06 Proceedings of the IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing
A new approach for epileptic seizure detection using adaptive neural network
Expert Systems with Applications: An International Journal
Adaptive neuro-fuzzy inference systems for analysis of internal carotid arterial Doppler signals
Computers in Biology and Medicine
Engineering Applications of Artificial Intelligence
Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients
Expert Systems with Applications: An International Journal
Artificial Intelligence in Medicine
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The electroencephalograph (EEG) signal is one of the most widely used signals in the biomedicine field due to its rich information about human tasks. This research study describes a new approach based on a fuzzy logic system implemented in the framework of a neural network for classification of EEG signals. In practical applications of pattern recognition, there are often diverse features extracted from raw data, which need recognising. 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 using the wavelet transform and classification using the classifier trained on the extracted features. The proposed network constructs its initial rules by clustering, while the final fuzzy rule base is determined by competitive learning. Both error backpropagation and recursive least squares estimation, are applied to the learning scheme. The performance of the model was evaluated in terms of training performance and high classification accuracies and the results confirmed that the proposed scheme has potential in classifying the EEG signals.