WASET
	@article{(Open Science Index):https://publications.waset.org/pdf/10002873,
	  title     = {Multi-Layer Perceptron Neural Network Classifier with Binary Particle Swarm Optimization Based Feature Selection for Brain-Computer Interfaces},
	  author    = {K. Akilandeswari and  G. M. Nasira},
	  country	= {},
	  institution	= {},
	  abstract     = {Brain-Computer Interfaces (BCIs) measure brain
signals activity, intentionally and unintentionally induced by users,
and provides a communication channel without depending on the
brain’s normal peripheral nerves and muscles output pathway.
Feature Selection (FS) is a global optimization machine learning
problem that reduces features, removes irrelevant and noisy data
resulting in acceptable recognition accuracy. It is a vital step
affecting pattern recognition system performance. This study presents
a new Binary Particle Swarm Optimization (BPSO) based feature
selection algorithm. Multi-layer Perceptron Neural Network
(MLPNN) classifier with backpropagation training algorithm and
Levenberg-Marquardt training algorithm classify selected features.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {9},
	  number    = {6},
	  year      = {2015},
	  pages     = {1608 - 1614},
	  ee        = {https://publications.waset.org/pdf/10002873},
	  url   	= {https://publications.waset.org/vol/102},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 102, 2015},
	}