Output details

Channel selection and classification of electroencephalogram signals: an artificial neural network and genetic algorithm-based approach.

Electroencephalogram-based brain–computer-interfaces (BCI) provide a new communication channel between the human brain and a computer. In this paper, new approaches to performing effective channel selections/reductions and pre-processing the data are developed and rigorously validated. The methods reduce the difficulties associated with data collection and greatly improve the generalization of the classifier. They should have applicability to BCI and other important areas of medical research that are heavily reliant on large and complex data sets. Methodology has been adopted by R. K. Tripathy et al. (Indonesian Journal of Electrical Engineering and Informatics http://portalgaruda.org/journals/index.php/IJEEI/article/view/59/pdf).