Analysis of EEG signals by implementing eigenvector methods/recurrent neural networks

Authors:
Elif Derya Übeyli
Affiliations:
Department of Electrical and Electronics Engineering, Faculty of Engineering, TOBB Ekonomi ve Teknoloji Üniversitesi, 06530 Söğütözü, Ankara, Turkey
Venue:
Digital Signal Processing
Year:
2009

Citing 9
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Abstract

The implementation of recurrent neural network (RNN) employing eigenvector methods 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 RNN. 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 RNN trained on these features achieved high classification accuracies.