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Learning to recognise mental activities

Agapitos, A and Dyson, M and Lucas, SM and Sepulveda, F (2008) Learning to recognise mental activities. In: Proceedings of the 10th annual conference on Genetic and evolutionary computation - GECCO '08, ? - ?.

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Two families (stateful and stateless) of genetically programmed classifiers were tested on a five class brain computer 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.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: _not provided_ - Notes:
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 03 Oct 2012 09:25
Last Modified: 23 Sep 2022 19:10

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