Evolutionary Weighted Ensemble for EEG Signal Recognition

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 298)


Recognition of an EEG signal is a very complex but very important problem. In this paper we focus on a simplified classification problem which consists of detection finger movement based on an analysis of seven EEG sensors. The signals gathered by each sensor are subsequently classified by the respective classification algorithm, which is based on data compression and so called LZ-Complexity. To improve overall accuracy of the system, the Evolutionary Weighted Ensemble (EWE) system is proposed. The parameters of the EWE are set in a learning procedure which uses an evolutionary algorithm tailored for that purpose. To take full advantage of information returned by sensor classifiers, setting negative weights are permitted, which significantly raises overall accuracy. Evaluation of EWE and its comparison against selected traditional ensemble algorithm is carried out using empirical data consisting of almost 5 hundred samples. The results show that the EWE algorithm exploits the knowledge represented by the sensor classifiers very effectively, and greatly improves classification accuracy.


EEG classification machine learning ensemble of classifiers evolutionary algorithms 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Konrad Jackowski
    • 1
    • 2
  • Jan Platos
    • 1
  • Michal Prilepok
    • 1
  1. 1.IT4InnovationsVSB Technical University of OstravaOstravaCzech Republic
  2. 2.Department of Systems and Computer NetworksWroclaw University of TechnologyWroclawPoland

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