Analysis and Classification of EEG Data: An Evaluation of Methods
Analysis and interpretation of electroencephalogram signals have found a wide spectrum of applications in clinical diagnosis. In spite of the outstanding experience of specialists, the analysis of biomedical data encounters many difficulties. Problems are associated with both technical aspects and nonstationary character of EEG sequences. Hardware and software solutions in this area are subjected to the continuous improvement due to the technological development. A very promising tool in analysis and interpretation of EEG signals are artificial neural networks. The paper presents the application of artificial neural networks along with the discrete wavelet transform to the analysis and classification of neurological disorders based on recorded EEG signals.
KeywordsMultiresolution Representation Nonstationary Character Multiresolution Signal Decomposition Projection Seizure Support Vector Machine Support
Unable to display preview. Download preview PDF.
- 2.Duda, R.O., Hart, P.E., Stork, D.H.: Pattern Classification, 2nd edn. Wiley Interscience (2000)Google Scholar
- 3.Emiliani, G.M.M., Frietman, E.E.E.: Automatic classification of EEGs with neural networks. Microelectronic Systems Integration 1(1), 41–62 (1994)Google Scholar
- 9.Rutkowski, G.: Artificial neural networks in the classification of EEG signals. In: Proceedings of XIII International Workshop OWD 2011, Krynica, Poland, October 22–25 (2011)Google Scholar