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
AbstractA 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).
|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 English||Algorithm 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|
|Publication indicators||= 0; = 10; = 26.0|
|Citation count*||28 (2020-01-20)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.