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Using mental tasks transitions detection to improve spontaneous mental activity classification

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Abstract

This paper presents an algorithm based on canonical variates transformation (CVT) and distance based discriminant analysis (DBDA) combined with a mental tasks transitions detector (MTTD) to classify spontaneous mental activities in order to operate a brain-computer interface working under an asynchronous protocol. The algorithm won the BCI Competition III -Data Set V: Multiclass Problem, Continous EEG- achieving an averaged classification accuracy over three subjects of 68.65% (79.60, 70.31 and 56.02%, respectively) in a three-class problem.

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References

  1. Blankertz B, Dornhege G, Lemm S, Krauledat M, Curio G, Müller K-R (2006) The Berlin brain-computer interface: machine learning based detection of user specific brain states. J Univers Comput Sci 12:581–607

    Google Scholar 

  2. Blankertz B, Müller K-R, Krusienski D, Schalk G, Wolpaw JR, Schlögl A, Pfurtscheller G, Millán J del R, Schröder M, Birbaumer N (2006) The BCI competition III: validating alternative approaches to actual BCI problems. IEEE Trans Neural Syst Rehab Eng 14:153-159

    Article  Google Scholar 

  3. Boostani R, Graimann B, Moradi MH, Pfurtscheller G (2007) A comparison approach toward finding the best feature and classifier in cue-based BCI. Med Biol Eng Comput 45:403–412

    Article  Google Scholar 

  4. Cuadras CM, Fortiana J, Oliva J (1997) The proximity of an individual to a population with applications in discriminant analysis. J Classif 14:117-136

    Article  MATH  MathSciNet  Google Scholar 

  5. Krzanowski WJ (1988) Principles of multivariate analysis. Oxford University Press, Oxford

    MATH  Google Scholar 

  6. Mahmoudi B, Erfanian A (2006) Electro-encephalogram based brain-computer interface: improved performance by mental practice and concentration skills. Med Biol Eng Comput 44:959–969

    Article  Google Scholar 

  7. Millán J del R (2004) On the need for on-line learning in brain-computer interfaces.In: Proceedings of international joint conference on neural networks

  8. Millán J del R, Renkens F, Mouriño J, Gerstner W (2004) Noninvasive brain-actuated control of a mobile robot by human EEG. IEEE Trans Biomed Eng 51:1026–1033

    Article  Google Scholar 

  9. Vaughan TM, Wolpaw JR (2006) The third international meeting on brain-computer interface technology: making a difference. IEEE Trans Rehab Eng 14:126–127

    Google Scholar 

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Acknowledgments

We thank the anonymous reviewers their contribution to improve the quality of the manuscript with their comments and Dr José del R. Millán for valuable discussions.

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Author notes

  1. Ferran Galán

    Present address: IDIAP Research Institute, Martigny, Switzerland

Authors and Affiliations

  1. Departament de Metodologia de les Ciències del Comportament, Facultat de Psicologia, Universitat de Barcelona, Barcelona, Spain

    Ferran Galán

  2. Departament d’Estadística, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain

    Francesc Oliva

  3. Departament de Metodologia de les Ciències del Comportament, Facultat de Psicologia, Universitat de Barcelona, Barcelona, Spain

    Joan Guàrdia

Authors
  1. Ferran Galán
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  2. Francesc Oliva
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  3. Joan Guàrdia
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Corresponding author

Correspondence to Ferran Galán.

Additional information

This work was supported in part by Agència de Gestió d’Ajuts Universitaris i de Recerca, Departament d’Universitats Recerca i Societat de la Informació, Generalitat de Catalunya, under Grants 2002FI00437, 2001SGR00139 and 2001SGR00067, and by Ministerio de Educación y Ciencia, under Grant MTM2004-00440.

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Galán, F., Oliva, F. & Guàrdia, J. Using mental tasks transitions detection to improve spontaneous mental activity classification. Med Bio Eng Comput 45, 603–609 (2007). https://doi.org/10.1007/s11517-007-0197-7

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  • DOI: https://doi.org/10.1007/s11517-007-0197-7

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