Selecting better EEG channels for classification of mental tasks

In this work a new method is proposed to reduce the number of EEG channels needed to classify mental tasks. By applying genetic algorithm to the search space consisting of 6 channel combinations of 19 EEG channels the more salient combinations of them in classification of three mental tasks are sele...

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Bibliographic Details
Published in2004 IEEE International Symposium on Circuits and Systems (ISCAS) Vol. 3; pp. III - 537
Main Authors Tavakolian, K., Nasrabadi, A.M., Rezaei, S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2004
Subjects
Application software
Backpropagation algorithms
Biological neural networks
Brain computer interfaces
Computer science
Data mining
Electroencephalography
Feature extraction
Genetic algorithms
Signal processing algorithms
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Summary:In this work a new method is proposed to reduce the number of EEG channels needed to classify mental tasks. By applying genetic algorithm to the search space consisting of 6 channel combinations of 19 EEG channels the more salient combinations of them in classification of three mental tasks are selected. This algorithm reduces the calculation time and the final results are verified by our observations. Obtained results bring forward the concept of systematic and intelligent selection criteria for choosing superior EEG channels of subjects for mental task classification. This may find applications in the field of brain computer interfaces which are based on classifications of mental tasks, by reducing the number of EEG channels.
ISBN:078038251X
9780780382510
DOI:10.1109/ISCAS.2004.1328802