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Characterization of the non-stationary nature of steady-state visual evoked potentials using echo state networks

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  • David Ibáñez-Soria
  • Aureli Soria-Frisch
  • Jordi Garcia-Ojalvo
  • Giulio Ruffini

Abstract

State Visual Evoked Potentials (SSVEPs) arise from a resonance phenomenon in the visual cortex that is produced by a repetitive visual stimulus. SSVEPs have long been considered a steady-state response resulting from purely oscillatory components phase locked with the stimulation source, matching the stimulation frequency and its harmonics. Here we explore the dynamical character of the SSVEP response by proposing a novel non-stationary methodology for SSVEP detection based on an ensemble of Echo State Networks (ESN). The performance of this dynamical approach is compared to stationary canonical correlation analysis (CCA) for the detection of 6 visual stimulation frequencies ranging from 12 to 22 Hz. ESN-based methodology outperformed CCA, achieving an average information transfer rate of 47 bits/minute when simulating a BCI system of 6 degrees of freedom. However, for some subjects and stimulation frequencies the detection accuracy of CCA exceeds that of ESN. The comparison suggests that each methodology captures different features of the SSVEP response: while CCA captures purely stationary patterns, the ESN-based approach presented here is capable of detecting the non-stationary nature of the SSVEP.

Suggested Citation

  • David Ibáñez-Soria & Aureli Soria-Frisch & Jordi Garcia-Ojalvo & Giulio Ruffini, 2019. "Characterization of the non-stationary nature of steady-state visual evoked potentials using echo state networks," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-13, July.
    • Handle: RePEc:plo:pone00:0218771
    DOI: 10.1371/journal.pone.0218771
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    References listed on IDEAS

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    1. Zafer İşcan & Vadim V Nikulin, 2018. "Steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) performance under different perturbations," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-17, January.
    2. Kuan, Chung-Ming & Liu, Tung, 1995. "Forecasting Exchange Rates Using Feedforward and Recurrent Neural Networks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(4), pages 347-364, Oct.-Dec..
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