Spatial filtering and selection of optimized components in four class motor imagery EEG data using independent components analysis

@article{Brunner2007SpatialFA,
  title={Spatial filtering and selection of optimized components in four class motor imagery EEG data using independent components analysis},
  author={Clemens Brunner and Muhammad Naeem and Robert Leeb and Bernhard Graimann and Gert Pfurtscheller},
  journal={Pattern Recognition Letters},
  year={2007},
  volume={28},
  pages={957-964}
}
Three independent components analysis (ICA) algorithms (Infomax, FastICA and SOBI) have been compared with other preprocessing methods in order to find out whether and to which extent spatial filtering of EEG data can improve single trial classification accuracy. As reference methods, common spatial patterns (CSP) (a supervised method, whereas all ICA algorithms are unsupervised), bipolar derivations and the original raw monopolar data were used. In addition to only performing ICA, the number… CONTINUE READING
BETA

Citations

Publications citing this paper.
SHOWING 1-10 OF 94 CITATIONS, ESTIMATED 25% COVERAGE

FILTER CITATIONS BY YEAR

2007
2019

CITATION STATISTICS

  • 13 Highly Influenced Citations

  • Averaged 9 Citations per year over the last 3 years

References

Publications referenced by this paper.
SHOWING 1-10 OF 20 REFERENCES

Similar Papers

Loading similar papers…