A New Method of EEG Classification for BCI with Feature Extraction Based on Higher Order Statistics of Wavelet Components and Selection with Genetic Algorithms

Marcin Kołodziej , Andrzej Majkowski , Remigiusz J Rak


A new method of feature extraction and selection of EEG signal for brain-computer interface design is presented. The proposed feature selection method is based on higher order statistics (HOS) calculated for the details of discrete wavelets transform (DWT) of EEG signal. Then a genetic algorithm is used for feature selection. During the experiment classification is conducted on a single trial of EEG signals. The proposed novel method of feature extraction using HOS and DWT gives more accurate results then the algorithm based on discrete Fourier transform (DFT).
Author Marcin Kołodziej (FoEE / ITEEMIS)
Marcin Kołodziej,,
- The Institute of the Theory of Electrical Engineering, Measurement and Information Systems
, Andrzej Majkowski (FoEE / ITEEMIS)
Andrzej Majkowski,,
- The Institute of the Theory of Electrical Engineering, Measurement and Information Systems
, Remigiusz J Rak - [Warsaw University of Technology (PW), MNiSW [80]]
Remigiusz J Rak,,
- Politechnika Warszawska
Book Dobnikar Andrej, Lotrič Uroš, Šter Branko (eds.): Adaptive and Natural Computing Algorithms, Lecture Notes In Computer Science, no. 6593, 2011, Springer Berlin Heidelberg, ISBN 978-3-642-20281-0, 978-3-642-20282-7
Keywords in EnglishAlgorithm Analysis and Problem Complexity, Artificial Intelligence (incl. Robotics), brain-computer interface (BCI), Computation by Abstract Devices, data-mining, discrete wavelet transform (DWT), feature extraction, feature selection, genetic algorithms (GA), higher order statistics (HOS), Image Processing and Computer Vision, Programming Techniques, Software Engineering
URL http://link.springer.com/chapter/10.1007/978-3-642-20282-7_29
Score (nominal)4
Publication indicators Scopus Citations = 0; WoS Citations = 10; GS Citations = 26.0
Citation count*28 (2020-01-20)
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