@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}, }