Selecting better EEG channels for classification of mental tasks

• Published in: 2004 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
• Language: English
• 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
• DOI: 10.1109/ISCAS.2004.1328802
• ISBN: 078038251X; 9780780382510

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.