Multi-Layer Perceptron Neural Network Classifier With Binary Particle Swarm Optimization Based Feature Selection For Brain-Computer Interfaces

K. Akilandeswari, G. M. Nasira
2015 Zenodo  
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
more » ... ents 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.
doi:10.5281/zenodo.1109968 fatcat:yga7s2bmzreyveummgta3baz74