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Broad-Band Visually Evoked Potentials: Re(con)volution in Brain-Computer Interfacing

Author

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  • Jordy Thielen
  • Philip van den Broek
  • Jason Farquhar
  • Peter Desain

Abstract

Brain-Computer Interfaces (BCIs) allow users to control devices and communicate by using brain activity only. BCIs based on broad-band visual stimulation can outperform BCIs using other stimulation paradigms. Visual stimulation with pseudo-random bit-sequences evokes specific Broad-Band Visually Evoked Potentials (BBVEPs) that can be reliably used in BCI for high-speed communication in speller applications. In this study, we report a novel paradigm for a BBVEP-based BCI that utilizes a generative framework to predict responses to broad-band stimulation sequences. In this study we designed a BBVEP-based BCI using modulated Gold codes to mark cells in a visual speller BCI. We defined a linear generative model that decomposes full responses into overlapping single-flash responses. These single-flash responses are used to predict responses to novel stimulation sequences, which in turn serve as templates for classification. The linear generative model explains on average 50% and up to 66% of the variance of responses to both seen and unseen sequences. In an online experiment, 12 participants tested a 6 × 6 matrix speller BCI. On average, an online accuracy of 86% was reached with trial lengths of 3.21 seconds. This corresponds to an Information Transfer Rate of 48 bits per minute (approximately 9 symbols per minute). This study indicates the potential to model and predict responses to broad-band stimulation. These predicted responses are proven to be well-suited as templates for a BBVEP-based BCI, thereby enabling communication and control by brain activity only.

Suggested Citation

  • Jordy Thielen & Philip van den Broek & Jason Farquhar & Peter Desain, 2015. "Broad-Band Visually Evoked Potentials: Re(con)volution in Brain-Computer Interfacing," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-22, July.
    • Handle: RePEc:plo:pone00:0133797
    DOI: 10.1371/journal.pone.0133797
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    Cited by:

    1. Jonas L Isaksen & Ali Mohebbi & Sadasivan Puthusserypady, 2017. "Optimal pseudorandom sequence selection for online c-VEP based BCI control applications," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-13, September.
    2. Zahra Shirzhiyan & Ahmadreza Keihani & Morteza Farahi & Elham Shamsi & Mina GolMohammadi & Amin Mahnam & Mohsen Reza Haidari & Amir Homayoun Jafari, 2019. "Introducing chaotic codes for the modulation of code modulated visual evoked potentials (c-VEP) in normal adults for visual fatigue reduction," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-29, March.
    3. Sebastian Nagel & Martin Spüler, 2018. "Modelling the brain response to arbitrary visual stimulation patterns for a flexible high-speed Brain-Computer Interface," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-16, October.
    4. Yonghui Liu & Qingguo Wei & Zongwu Lu, 2018. "A multi-target brain-computer interface based on code modulated visual evoked potentials," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-17, August.

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