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Adaptive Methods in BCI Research - An Introductory Tutorial

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Brain-Computer Interfaces

Part of the book series: The Frontiers Collection ((FRONTCOLL))

Abstract

This chapter tackles a difficult challenge: presenting signal processing material to non-experts. This chapter is meant to be comprehensible to people who have some math background, including a course in linear algebra and basic statistics, but do not specialize in mathematics, engineering, or related fields. Some formulas assume the reader is familiar with matrices and basic matrix operations, but not more advanced material. Furthermore, we tried to make the chapter readable even if you skip the formulas. Nevertheless, we include some simple methods to demonstrate the basics of adaptive data processing, then we proceed with some advanced methods that are fundamental in adaptive signal processing, and are likely to be useful in a variety of applications. The advanced algorithms are also online available [30]. In the second part, these techniques are applied to some real-world BCI data.

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Notes

  1. 1.

    The O-notation is frequently used in computer science to show the growth rate of an algorithm’s usage of computational resources with respect to its input

References

  1. Block matrix descompositions (2008–2010). http://ccrma.stanford.edu/~jos/lattice/Block_matrix_decompositions.html. Accessed on Sept 2010.

  2. N. Birbaumer et al., The thought-translation device (TTD): Neurobehavioral mechanisms and clinical outcome. IEEE Trans Neural Syst Rehabil Eng, 11(2), 120–123, (2003).

    Article  PubMed  Google Scholar 

  3. B. Blankertz et al., The BCI competition. III: Validating alternative approaches to actual BCI problems. IEEE Trans Neural Syst Rehabil Eng, 14(2), 153–159, (2006).

    Article  PubMed  Google Scholar 

  4. B. Blankertz et al., Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Proc Mag, 25(1), 41–56, (2008).

    Article  Google Scholar 

  5. del R. Millán and Mouriño J.del, R. Millán and J. Mouriño, Asynchronous BCI and local neural classifiers: An overview of the adaptive brain interface project. IEEE Trans Neural Syst Rehabil Eng 11(2), 159–161, (2003).

    Article  Google Scholar 

  6. G. Dornhege et al., Combining features for BCI. In S. Becker, S. Thrun, and K. Obermayer (Eds.), Advances in neural information processing systems, MIT Press, Cambridge, MA, pp. 1115–1122, (2003).

    Google Scholar 

  7. G. Dornhege et al., Combined optimization of spatial and temporal filters for improving brain-computer interfacing. IEEE Trans Biomed Eng, 53(11), 2274–2281, (2006).

    Article  PubMed  Google Scholar 

  8. G. Dornhege et al., Toward brain-computer interfacing, MIT Press, Cambridge, MA, (2007).

    Google Scholar 

  9. S. Haykin, Adaptive filter theory, Prentice Hall International, New Jersey, (1996).

    Google Scholar 

  10. B. Hjorth, “EEG analysis based on time domain properties.” Electroencephalogr Clin Neurophysiol, 29(3), 306–310, (1970).

    Article  CAS  PubMed  Google Scholar 

  11. I.I. Goncharova and J.S. Barlow, Changes in EEG mean frequency and spectral purity during spontaneous alpha blocking. Electroencephalogr Clin Neurophysiol, 76(3), 197–204; Adaptive methods in BCI research – an introductory tutorial 25, (1990).

    Article  CAS  PubMed  Google Scholar 

  12. R. Kalman and E. Kalman, A new approach to Linear Filtering and Prediction Theory. J Basic Eng Trans ASME, 82, 34–45, (1960).

    Google Scholar 

  13. B.R.E. Kalman, and R.S. Bucy, New results on linear filtering and prediction theory. J Basic Eng, 83, 95–108, (1961).

    Google Scholar 

  14. K. M. Krauledat, Analysis of nonstationarities in EEG signals for improving Brain-Computer Interface performance, Ph. D. diss. Technische Universität Berlin, Fakultät IV – Elektrotechnik und Informatik, (2008).

    Google Scholar 

  15. G. Krausz et al., Critical decision-speed and information transfer in the ‘graz brain-computer-interface’. Appl Psychophysiol Biofeedback, 28, 233–240, (2003).

    Article  CAS  PubMed  Google Scholar 

  16. S. Lemm et al. Spatio-spectral filters for robust classification of single-trial EEG. IEEE Trans Biomed Eng, 52(9), 1541–48, (2005).

    Article  PubMed  Google Scholar 

  17. A. Li, G. Yuanqing, K. Li, K. Ang, and C. Guan, Digital signal processing and machine learning. In B. Graimann, B. Allision, and G. Pfurtscheller (Eds.), Advances in neural information processing systems, Springer, New York, (2009).

    Google Scholar 

  18. J. McFarland, A.T. Lefkowicz, and J.R. Wolpaw, Design and operation of an EEG-based brain-computer interface with digital signal processing technology. Behav Res Methods, Instruments Comput, 29, 337–345, (1997).

    Google Scholar 

  19. D.J. McFarland et al., BCI meeting 2005-workshop on BCI signal processing: feature extraction and translation.” IEEE Trans Neural Syst Rehabil Eng 14(2), 135–138, (2006).

    Article  PubMed  Google Scholar 

  20. R.J. Meinhold and N.D. Singpurwalla, Understanding Kalman filtering. Am Statistician, 37, 123–127, (1983).

    Article  Google Scholar 

  21. E. Oja and J. Karhunen, On stochastic approximation of the eigenvectors and eigenvalues of the expectation of a random matrix. J Math Anal Appl, 106, 69–84, (1985).

    Article  Google Scholar 

  22. K. Pawelzik, J. Kohlmorgen and K.-R. Muller, Annealed competition of experts for a segmentation and classification of switching dynamics. Neural Comput, 8, 340–356, (1996).

    Article  Google Scholar 

  23. G. Pfurtscheller et al., On-line EEG classification during externally-paced hand movements using a neural network-based classifier. Electroencephalogr Clin Neurophysiol, 99(5), 416–25, (1996).

    Article  CAS  PubMed  Google Scholar 

  24. N.G. Pfurtscheller and C. Neuper, Motor imagery and direct brain-computer communications, Proceedings IEEE 89, 1123–1134, (2001).

    Article  Google Scholar 

  25. G. Pfurtscheller et al., EEG-based discrimination between imagination of right and left hand movement. Electroencephalogr Clin Neurophysiol, 103, 642–651, (1997).

    Article  CAS  PubMed  Google Scholar 

  26. G. Pfurtscheller et al., Current trends in Graz brain-computer interface (BCI) research. IEEE Trans Rehabil Eng, 8, 216–219, (2000).

    Article  CAS  PubMed  Google Scholar 

  27. G. Pfurtscheller et al., “Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters.” IEEE Trans Rehabil Eng, 6(3), 316–25, (1998).

    Article  CAS  PubMed  Google Scholar 

  28. N. Pop-Jordanova and J. Pop-Jordanov, Spectrum-weighted EEG frequency (Brainrate) as a quantitative indicator of arousal Contributions. Sec Biol Med Sci XXVI(2), 35–42, (2005).

    Google Scholar 

  29. H. Ramoser, J. Müller-Gerking, G. Pfurtscheller, “Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans Rehabil Eng. 8(4), 441–446, (2000).

    Article  CAS  PubMed  Google Scholar 

  30. A. Schlogl, BioSig – an open source software library for biomedical signal processing, http://biosig.sf.net, 2003–2010.

  31. A. Schlogl, D. Flotzinger and G. Pfurtscheller, Adaptive autoregressive modeling used for single-trial EEG classification. Biomedizinische Technik, 42, 162–167, (1997).

    CAS  PubMed  Google Scholar 

  32. A. Schlögl et al. “Information transfer of an EEG-based brancomputer interface.” First international IEEE EMBS conference on neural engineering, 2003, 641–644, (2003).

    Article  Google Scholar 

  33. A. Schlögl et al. “Characterization of four-class motor imagery eeg data for the bci-competition 2005. J Neural Eng, 2(4), L14–L22, (1997).

    Article  Google Scholar 

  34. C. Neuper and G. Pfurtscheller, Estimating the mutual information of an EEG-based brain-computer-interface. Biomedizinische Technik, 47(1–2), 3–8, (2002).

    PubMed  Google Scholar 

  35. S.J. Roberts and G. Pfurtscheller, A criterion for adaptive autoregressive models. Proceedings Ann Int Conf IEEE Eng Med Biol, 2, 1581–1582, (2000).

    Google Scholar 

  36. A. Schlögl, The electroencephalogram and the adaptive autoregressive model: theory and applications, Shaker Verlag, Aachen, Germany, (2000).

    Google Scholar 

  37. M.P. Shenoy, R. P. N. Rao, M. Krauledat, B. Blankertz and K.-R. Müller, Towards adaptive classification for BCI. J Neural Eng, 3(1), R13–R23, (2006).

    Article  PubMed  Google Scholar 

  38. H. Shimodaira, Improving predictive inference under covariate shift by weighting the log likelihood function. J Stat Plan Inference, 90, 227–244, (2000).

    Article  Google Scholar 

  39. V. Solo and X. Kong, Performance analysis of adaptive eigenanalysis algorithms, IEEE Trans Signal Process, 46(3), 636–46, (1998).

    Article  Google Scholar 

  40. M. Sugiyama, M. Krauledat and K.-R. Müller, Covariate shift adaptation by importance weighted cross validation. J Mach Learning Res 8, 1027–1061, (2007).

    Google Scholar 

  41. M.M. Sugiyama and K.-R. Müller, Input-dependent estimation of generalization error under covariate shift. Stat Decis, 23(4), 249–279, (2005).

    Article  Google Scholar 

  42. S. Sun and C. Zhang, Adaptive feature extraction for EEG signal classification. Med Bio Eng Comput, 44(2), 931–935, (2006).

    Article  Google Scholar 

  43. R. Tomioka et al., Adapting spatial filtering methods for nonstationary BCIs. Proceedings of 2006 Workshop on Information-Based Induction Sciences (IBIS2006), 2006, 65–70, (2006).

    Google Scholar 

  44. C. Vidaurre et al., About adaptive classifiers for brain computer interfaces. Biomedizinische Technik, 49(Special Issue 1), 85–86, (2004).

    Google Scholar 

  45. C. Vidaurre et al., A fully on-line adaptive brain computer interface. Biomedizinische Technik, 49(Special Issue 2), 760–761, (2004).

    Google Scholar 

  46. C. Vidaurre et al., Adaptive on-line classification for EEG-based brain computer interfaces with AAR parameters and band power estimates. Biomedizinische Technik, 50, 350–354. Adaptive methods in BCI research – an introductory tutorial, 27, (2005).

    Article  CAS  PubMed  Google Scholar 

  47. C. Vidaurre et al., A fully on-line adaptive BCI, IEEE Trans Biomed Eng, 53, 1214–1219, (2006).

    Article  CAS  PubMed  Google Scholar 

  48. C. Vidaurre et al., Study of on-line adaptive discriminant analysis for EEG-based brain computer interfaces. IEEE Trans Biomed Eng, 54, 550–556, (2007).

    Article  CAS  PubMed  Google Scholar 

  49. J. Wackermann, Towards a quantitative characterization of functional states of the brain: from the non-linear methodology to the global linear descriptor. Int J Psychophysiol, 34, 65–80, (1999).

    Article  CAS  PubMed  Google Scholar 

  50. J.R. Wolpaw et al., Brain-computer interface technology: A review of the first international meeting. IEEE Trans Neural Syst Rehabil Eng, 8(2), 164–173, (2000).

    CAS  Google Scholar 

  51. J.R. Wolpaw et al., Brain-computer interfaces for communication and control.” Clin Neurophysiol, 113, 767–791, (2002).

    Article  PubMed  Google Scholar 

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Acknowledgments

This work was supported by the EU grants “BrainCom” (FP6-2004-Mobility-5 Grant No 024259) and “Multi-adaptive BCI” (MEIF-CT-2006 Grant No 040666). Furthermore, we thank Matthias Krauledat for fruitful discussions and tools for generating Fig. 5.

Author information

Authors and Affiliations

  1. Institute of Science and Technology Austria (IST Austria), Am Campus 1, A–3400, Klosterneuburg, Austria

    Alois Schlögl

  2. Machine Learning Laboratory, Berlin Institute of Technology, Berlin, Germany

    Carmen Vidaurre & Klaus-Robert Müller

Authors
  1. Alois Schlögl
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  2. Carmen Vidaurre
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  3. Klaus-Robert Müller
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Corresponding author

Correspondence to Alois Schlögl .

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Editors and Affiliations

  1. Otto Bock HealthCare GmbH, Max-Näder-Str. 15, Duderstadt, 37115, Germany

    Bernhard Graimann

  2. Institut für Semantische Datenanalyse/, Knowledge Discovery, Technische Universität Graz, Krenngasse 37, Graz, 8010, Austria

    Gert Pfurtscheller

  3. Inst. Semantische Datenanalyse, Brain-Computer Interface Labor (BCI), TU Graz, Krenngasse 37/III, Graz, 8010, Austria

    Brendan Allison

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Schlögl, A., Vidaurre, C., Müller, KR. (2009). Adaptive Methods in BCI Research - An Introductory Tutorial. In: Graimann, B., Pfurtscheller, G., Allison, B. (eds) Brain-Computer Interfaces. The Frontiers Collection. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02091-9_18

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  • DOI: https://doi.org/10.1007/978-3-642-02091-9_18

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02090-2

  • Online ISBN: 978-3-642-02091-9

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