A Hybrid Nonlinear-Discriminant Analysis Feature Projection Technique
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Multiplying the mileage of your dataset with subwindowing
BI'11 Proceedings of the 2011 international conference on Brain informatics
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The high dimensional nature of EEG data due to large electrode numbers and long task periods is one of the main challenges of studying EEG. Evolutionary alternatives to conventional dimension reduction methods exhibit the advantage of not requiring the entire recording sessions for operation. Particle Swarm Optimization (PSO) is an Evolutionary method that achieves performance through evaluation of several generations of possible solutions. This study investigates the feasibility of a 2 layer PSO structure for synchronous reduction of both electrode and task period dimensions using 4 motor imagery EEG data. The results indicate the potential of the proposed PSO paradigm for dimension reduction with insignificant losses in classification and the practical uses in subject transfer applications.