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- W98430877 abstract "Since electroencephalogram (EEG) is one of the most important sources of information in therapy of epilepsy, several researchers tried to address the issue of decision support for such a data. This research study describes the application of a novel fuzzy logic system implemented in the framework of a neural network for classification of EEG signals. Decision making was performed in two stages: initially, a feature extraction scheme using the wavelet transform (WT) has been applied and then a learning-based algorithm classifier performed the classification. 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 classification accuracies and the results confirmed that the proposed scheme has potential in classifying the EEG signals." @default.
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- W98430877 date "2010-01-01" @default.
- W98430877 modified "2023-10-16" @default.
- W98430877 title "Adaptive Fuzzy Inference Neural Network System for EEG Signal Classification" @default.
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- W98430877 doi "https://doi.org/10.1007/978-3-642-13639-9_18" @default.
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