Re(con)volution: Accurate Response Prediction for Broad-Band Evoked Potentials-Based Brain Computer Interfaces

Part of the SpringerBriefs in Electrical and Computer Engineering book series (BRIEFSELECTRIC)


Broad-band evoked potentials (BBEPs) are responses to non-periodic stimuli, evoked in reponse to carefully chosen pseudo-random noise-sequences (PRNS). In this chapter, a generative method called reconvolution is discussed. Reconvolution is a method that decomposes BBEPs in response to PRNS into transient responses to the individual events. With reconvolution, one can then generate responses to any other novel PRNS. In this chapter, we discuss three approaches to reconvolution: (1) a sequential approach, (2) an integrated approach, and (3) a zero-training approach. Reconvolution enables Brain Computer Interfaces to be trained with little or even no data, and provides a generative model to accurately predict responses to novel stimuli.


  1. 1.
    Sutter EE. (1984) The visual evoked response as a communication channel. In: Proceedings of the IEEE Symposium on Biosensors. pp 95–100Google Scholar
  2. 2.
    Sutter EE (1992) The brain response interface: communication through visually-induced electrical brain responses. J Microcomput Appl 15(1):31–45. doi: 10.1016/0745-7138(92)90045-7 CrossRefGoogle Scholar
  3. 3.
    Bin G, Gao X, Wang Y, Hong B, Gao S (2009) VEP-based brain-computer interfaces: time, frequency, and code modulations [Research Frontier]. Comput Intell Mag IEEE 4(4):22–26. doi: 10.1109/MCI.2009.934562 CrossRefGoogle Scholar
  4. 4.
    Bin G, Gao X, Wang Y, Li Y, Hong B, Gao S (2011) A high-speed BCI based on code modulation VEP. J Neural Eng 8(2):025015. doi: 10.1088/1741-2560/8/2/025015 PMID: 21436527CrossRefGoogle Scholar
  5. 5.
    Spüler M, Rosenstiel W, Bogdan M (2012) One class SVM and canonical correlation analysis increase performance in a c-VEP based brain-computer interface (BCI). In: Proceedings of 20th European Symposium on Artificial Neural Networks (ESANN 2012). Bruges, Belgium, pp 103–108Google Scholar
  6. 6.
    Spüler M, Rosenstiel W, Bogdan M (2012) Online adaptation of a c-VEP brain-computer interface (BCI) based on error-related potentials and unsupervised learning. PLoS ONE 7(12):e51077. doi: 10.1371/journal.pone.0051077 PMID: 23236433CrossRefGoogle Scholar
  7. 7.
    Thielen J, van den Broek P, Farquhar J, Desain P (2015) Broad-band visually evoked potentials: re(con)volution in brain-computer interfacing. PLoS ONE 10(7):e0133797. doi: 10.1371/journal.pone.0133797 CrossRefGoogle Scholar
  8. 8.
    Golomb SW, Welch LR, Goldstein RM, Hales AW (1982) Shift register sequences. Aegean Park Press, Laguna Hills, CA, p 78Google Scholar
  9. 9.
    Capilla A, Pazo-Alvarez P, Darriba A, Campo P, Gross J (2011) Steady-state visual evoked potentials can be explained by temporal superposition of transient event-related responses. PLoS ONE 6(1):e14543. doi: 10.1371/journal.pone.0014543 PMID: 21267081CrossRefGoogle Scholar
  10. 10.
    Farquhar J, Blankespoor J, Vlek R, Desain P (2008) Towards a noise-tagging auditory BCI-paradigm. In: Proceedings of the 4th Int BCI Workshop and Training Course 2008. Graz, Austria, pp 50–55Google Scholar
  11. 11.
    Gold R (1967) Optimal binary sequences for spread spectrum multi-plexing. IEEE Trans Inf Theory 13:619–621. doi: 10.1109/TIT.1967.1054048 CrossRefzbMATHGoogle Scholar

Copyright information

© The Author(s) 2017

Authors and Affiliations

  1. 1.Donders Institute for Brain, Cognition, and Behaviour, Radboud University NijmegenNijmegenNetherlands

Personalised recommendations