TY - JFULL AU - K. Akilandeswari and G. M. Nasira PY - 2015/7/ TI - Multi-Layer Perceptron Neural Network Classifier with Binary Particle Swarm Optimization Based Feature Selection for Brain-Computer Interfaces T2 - International Journal of Computer and Information Engineering SP - 1607 EP - 1614 VL - 9 SN - 1307-6892 UR - https://publications.waset.org/pdf/10002873 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 102, 2015 N2 - 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. ER -