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Introducing chaotic codes for the modulation of code modulated visual evoked potentials (c-VEP) in normal adults for visual fatigue reduction

Author

Listed:
  • Zahra Shirzhiyan
  • Ahmadreza Keihani
  • Morteza Farahi
  • Elham Shamsi
  • Mina GolMohammadi
  • Amin Mahnam
  • Mohsen Reza Haidari
  • Amir Homayoun Jafari

Abstract

Code modulated Visual Evoked Potentials (c-VEP) based BCI studies usually employ m-sequences as a modulating codes for their broadband spectrum and correlation property. However, subjective fatigue of the presented codes has been a problem. In this study, we introduce chaotic codes containing broadband spectrum and similar correlation property. We examined whether the introduced chaotic codes could be decoded from EEG signals and also compared the subjective fatigue level with m-sequence codes in normal subjects. We generated chaotic code from one-dimensional logistic map and used it with conventional 31-bit m-sequence code. In a c-VEP based study in normal subjects (n = 44, 21 females) we presented these codes visually and recorded EEG signals from the corresponding codes for their four lagged versions. Canonical correlation analysis (CCA) and spatiotemporal beamforming (STB) methods were used for target identification and comparison of responses. Additionally, we compared the subjective self-declared fatigue using VAS caused by presented m-sequence and chaotic codes. The introduced chaotic code was decoded from EEG responses with CCA and STB methods. The maximum total accuracy values of 93.6 ± 11.9% and 94 ± 14.4% were achieved with STB method for chaotic and m-sequence codes for all subjects respectively. The achieved accuracies in all subjects were not significantly different in m-sequence and chaotic codes. There was significant reduction in subjective fatigue caused by chaotic codes compared to the m-sequence codes. Both m-sequence and chaotic codes were similar in their accuracies as evaluated by CCA and STB methods. The chaotic codes significantly reduced subjective fatigue compared to the m-sequence codes.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0213197
    DOI: 10.1371/journal.pone.0213197
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Nikos K. Logothetis & Jon Pauls & Mark Augath & Torsten Trinath & Axel Oeltermann, 2001. "Neurophysiological investigation of the basis of the fMRI signal," Nature, Nature, vol. 412(6843), pages 150-157, July.
    4. Benjamin Wittevrongel & Marc M Van Hulle, 2016. "Frequency- and Phase Encoded SSVEP Using Spatiotemporal Beamforming," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-18, August.
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    1. Jose-Cruz Nuñez-Perez & Vincent-Ademola Adeyemi & Yuma Sandoval-Ibarra & Francisco-Javier Perez-Pinal & Esteban Tlelo-Cuautle, 2021. "Maximizing the Chaotic Behavior of Fractional Order Chen System by Evolutionary Algorithms," Mathematics, MDPI, vol. 9(11), pages 1-22, May.
    2. Felix Gembler & Piotr Stawicki & Abdul Saboor & Ivan Volosyak, 2019. "Dynamic time window mechanism for time synchronous VEP-based BCIs—Performance evaluation with a dictionary-supported BCI speller employing SSVEP and c-VEP," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-18, June.

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