Abstract
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.
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Atyabi, A., Luerssen, M.H., Fitzgibbon, S.P., Powers, D.M.W. (2012). The Impact of PSO Based Dimension Reduction on EEG Classification. In: Zanzotto, F.M., Tsumoto, S., Taatgen, N., Yao, Y. (eds) Brain Informatics. BI 2012. Lecture Notes in Computer Science(), vol 7670. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35139-6_21
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DOI: https://doi.org/10.1007/978-3-642-35139-6_21
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