Using genetic algorithms and k-nearest neighbour for automatic frequency band selection for signal classification

D. Rivero, L. Guo, J. A. Seoane, J. Dorado

Research output: Contribution to journalArticle (Academic Journal)

13 Citations (Scopus)


The classification of signals is usually based on the extraction of various features that subsequently will be used as an input to a classifier. These features are extracted as a result of the experts' prior knowledge, which may often involve a lack of the information necessary for an accurate classification in all cases. This study proposes a new technique, in which a genetic algorithm is used to automatically extract frequency-domain features from a set of signals, with no need of prior knowledge. This allows, first, to achieve greater accuracy in the classification of signals, and, secondly, to discover new data on the signals to be classified. This system was used to solve a well-known problem: classification of electroencephalogram (EEG) signals, and its results show a better performance in comparison with other works on the same problem.

Original languageEnglish
Pages (from-to)186-194
Number of pages9
JournalIET Signal Processing
Issue number3
Publication statusPublished - May 2012

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