Location of Repository

Learning to Recognise Mental Activities: Genetic Programming of Stateful Classifiers for Brain-Computer Interfacing

By Ros Agapitos, Matthew Dyson, Simon M. Lucas and Francisco Sepulveda

Abstract

Two families (stateful and stateless) of genetically programmed classifiers were tested on a five class braincomputer interface (BCI) data set of raw EEG signals. The ability of evolved classifiers to discriminate mental tasks from each other were analysed in terms of accuracy, precision and recall. A model describing the dynamics of state usage in stateful programs is introduced. An investigation of relationships between the model attributes and associated classification results was made. The results show that both stateful and stateless programs can be successfully evolved for this task, though stateful programs start from lower fitness and take longer to evolve

Topics: Algorithms, Performance, Experimentation Keywords Brain Computer Interface, Classification on Raw Signal, Stateful Representation, Statistical Signal Primitives
Year: 2010
OAI identifier: oai:CiteSeerX.psu:10.1.1.163.1545
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://citeseerx.ist.psu.edu/v... (external link)
  • http://www.cs.bham.ac.uk/~wbl/... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.