BibTeX:

@article{IJIRSTV2I1083,
     title={An Improved Multilayer Perceptron for BCI with Evolutionary Optimization},
     author={G.V.Sridhar and Dr.P.Mallikarjuna Rao},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={2},
     number={1},
     pages={328--332},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV2I1083.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}

Brain Computer Interface (BCI) research is based on recording and analyzing electroencephalographic (EEG) data and recognizing EEG patterns associated with various mental states. BCIs had become an active research area in the last decade. The ability of The Multilayer Perceptron Neural Networks (MLPNN) to model non-linear relationship is appropriate to model the complex nature of EEG signals. MLPNN’s convergence rate is relatively slow and it often yields sub-optimal solutions. In this paper, it is proposed to enhance the classification ability of the MLPNN incorporating Particle Swarm Optimization (PSO). This optimization helps to overcome the slow convergence rate and to avoid local minimum. The features of motor imagery in the frequency domain are extracted using Hilbert transform, and Principal Component Analysis is used for feature reduction. This is classified by MLPNN modified with PSO.

Brain Computer Interface (BCI), EEG, Multilayer Perceptron (MLP), Particle Swarm Optimization (PSO)