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Analysis and Classification of EEG Data: An Evaluation of Methods

  • Krzysztof Patan
  • Grzegorz Rutkowski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7268)

Abstract

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.

Keywords

Multiresolution Representation Nonstationary Character Multiresolution Signal Decomposition Projection Seizure Support Vector Machine Support 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Krzysztof Patan
    • 1
  • Grzegorz Rutkowski
    • 2
  1. 1.Institute of Control and Computation EngineeringUniversity of Zielona GóraPoland
  2. 2.Faculty of Electrical Engineering, Computer Science and TelecommunicationUniversity of Zielona GóraPoland

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