Using distributed genetic programming to evolve classifiers for a brain computer interface

The objective of this paper is to illustrate the application of genetic programming to evolve classifiers for multi-channel time series data. The paper shows how high performance distributed genetic programming (GP) has been implemented for evolving classifiers. The particular application discussed herein is the classification of human electroencephalographic (EEG) signals for a brain-computer interface (BCI). The resulting classifying structures provide classification rates comparable to those obtained using traditional, human-designed, classification methods.

• Type: Conference
• Year: 2006
• Where: ESANN
• Authors: Eva Alfaro-Cid, Anna Esparcia-Alcázar, Ken Sharman
• Keywords: ESANN 2006, ESANN 2007, Genetic Programming, Multi-channel Time Series, Structures Provide Classification