Comparison of designs towards a subject-independent brain-computer interface based on motor imagery

@article{Lotte2009ComparisonOD,
  title={Comparison of designs towards a subject-independent brain-computer interface based on motor imagery},
  author={Fabien Lotte and Cuntai Guan and Kai Keng Ang},
  journal={2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
  year={2009},
  pages={4543-4546}
}
A major limitation of current Brain-Computer Interfaces (BCI) based on Motor Imagery (MI) is that they are subject-specific BCI, which require data recording and system training for each new user. This process is time consuming and inconvenient, especially for casual users or portable BCI with limited computational resources. In this paper, we explore the design of a Subject-Independent (SI) MI-based BCI, i.e., a BCI that can be used immediately by any new user without training the BCI with the… CONTINUE READING

Tables, Results, and Topics from this paper.

Key Quantitative Results

  • Using LDA with 20 pairs of features, MR FBCSP reached an average accuracy of about 71 %, which was the best score among all feature extraction methods and classifiers.
  • Overall, combining MR FBCSP with LDA achieved a promising average accuracy of about 71 % for a SI-BCI design whereas the best SS-BCI design obtained an average accuracy of about 82 %.

Citations

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References

Publications referenced by this paper.
SHOWING 1-10 OF 16 REFERENCES

Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface

  • 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)
  • 2008
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Pattern Recognition, second edition

R. O. Duda, P. E. Hart, D. G. Stork
  • WILEY-INTERSCIENCE,
  • 2001
VIEW 5 EXCERPTS
HIGHLY INFLUENTIAL

Optimal spatial filtering of single trial EEG during imagined hand movement.

  • IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
  • 2000
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Boosting-based subject-independent brain computer interface

S. Lu, C. Guan
  • In ICPR,
  • 2008
VIEW 2 EXCERPTS

Unsupervised brain computer interface based on inter-subject information

  • 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
  • 2008
VIEW 1 EXCERPT

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