A decision support system for EEG signals based on adaptive fuzzy inference neural networks

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.