The impact of PSO based dimension reduction on EEG classification

作者： Adham Atyabi , Martin H. Luerssen , Sean P. Fitzgibbon , David M. W. Powers

关键词: Machine learning 、 Dimensionality reduction 、 Task (project management) 、 Eeg classification 、 Artificial intelligence 、 Brain–computer interface 、 Particle swarm optimization 、 Pattern recognition 、 Motor imagery 、 Electroencephalography 、 Computer science 、 Reduction (complexity)

摘要: The high dimensional nature of EEG data due to large electrode numbers and long task periods is one the main challenges studying EEG. Evolutionary alternatives conventional dimension reduction methods exhibit advantage not requiring entire recording sessions for operation. Particle Swarm Optimization (PSO) an method that achieves performance through evaluation several generations possible solutions. This study investigates feasibility a 2 layer PSO structure synchronous both period dimensions using 4 motor imagery data. results indicate potential proposed paradigm with insignificant losses in classification practical uses subject transfer applications.

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The impact of PSO based dimension reduction on EEG classification

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The impact of PSO based dimension reduction on EEG classification

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