Feature Selection for Brain-Computer Interfaces

@inproceedings{Koprinska2009FeatureSF,
  title={Feature Selection for Brain-Computer Interfaces},
  author={Irena Koprinska},
  booktitle={PAKDD Workshops},
  year={2009}
}
In this paper we empirically evaluate feature selection methods for classification of Brain-Computer Interface (BCI) data. We selected five state-of the-art methods, suitable for the noisy, correlated and highly dimensional BCI data, namely: information gain ranking, correlation-based feature selection, ReliefF, consistency-based feature selection and 1R ranking. We tested them with ten classification algorithms, representing different learning paradigms, on a benchmark BCI competition dataset… CONTINUE READING
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Comparison of Feature Selection Methods for Classification of BrainComputer Interface Data

  • I. Koprinska
  • Workshop on Advances and Issues in Biomedical…
  • 2009
1 Excerpt

Toward Brain-Computer Interfacing

  • G. Dornhege, Millán, +4 authors K.-R.
  • MIT Press, Cambridge
  • 2007
1 Excerpt

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