International Journal of Information and Communication Technology Research

International Journal of Information and Communication Technology Research>> Call for Papers(CFP)>>Volume 7, Number 7, July 2017

International Journal of Information and Communication Technology Research

Brain emotional learning based Brain Computer Interface with Independent Component Analysis

Full Text Pdf Pdf
Author(s) Abbas. Najafizadeh, Abdolreza Asadi Ghanbari
On Pages 283-289
Volume No. 3
Issue No. 10
Issue Date October, 2013
Publishing Date October, 2013
Keywords brain emotional learning (BEL); Genetic Algorithm; components Selection; Independent component analysis.


Key performance characteristics of BCI systems are speed (i.e., how long it takes to make a selection) and precision (i.e., how often the executed selection is the one the user intended). Current systems allow for one selection within several seconds at a relatively high accuracy. Expressed in bit rate, which combines both speed and accuracy, the sustained performance of typical non-invasive and invasive BCI systems is still modest. The generation performance of a brain computer interface depends largely on the signal to noise ratio and translation algorithms. Current BCIs have low information transfer rates. Artifact and Redundancy with acquired data two another major reasons for this limited capacity of Current BCIs. Artifacts are undesired signals that can introduce significant changes in brain signals and ultimately affect the neurological phenomenon. In new BCI systems for increase accuracy, increased number of electrodes. In this case the increased number of electrodes causes a non-linear increase Redundancy. This article used Genetic Algorithm and independent component analysis (ICA) for select The Effective components of EEG signal and Redundancy Reduction. The experimental results show that the proposed approach has the superior performance to the traditional filtering method and is applicable in new BCI systems. Another major reason for the modest bit rate is translation algorithm. In this paper, we introduce adaptive classifiers for classify electroencephalogram (EEG) signals. The adaptive classifier is brain emotional learning based adaptive classifier (BELBAC), which is based on emotional learning process.

    Journal of Information and Communication Technology | Journal of Science and Technology     
2012 IRPN Publishers