IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0213197.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0213197
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0213197&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0213197?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. Doungmo Goufo, Emile F. & Mbehou, Mohamed & Kamga Pene, Morgan M., 2018. "A peculiar application of Atangana–Baleanu fractional derivative in neuroscience: Chaotic burst dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 115(C), pages 170-176.
    4. Irene Neuner & Wolfram Kawohl & Jorge Arrubla & Tracy Warbrick & Konrad Hitz & Christine Wyss & Frank Boers & N Jon Shah, 2014. "Cortical Response Variation with Different Sound Pressure Levels: A Combined Event-Related Potentials and fMRI Study," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-14, October.
    5. Zvi N. Roth & Kendrick Kay & Elisha P. Merriam, 2022. "Natural scene sampling reveals reliable coarse-scale orientation tuning in human V1," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    6. Phoebe Koundouri & Barbara Hammer & Ulrike Kuhl & Alina Velias, 2022. "Behavioral and Neuroeconomics of Environmental Values," DEOS Working Papers 2227, Athens University of Economics and Business.
    7. Wan-Yu Shih & Hsiang-Yu Yu & Cheng-Chia Lee & Chien-Chen Chou & Chien Chen & Paul W. Glimcher & Shih-Wei Wu, 2023. "Electrophysiological population dynamics reveal context dependencies during decision making in human frontal cortex," Nature Communications, Nature, vol. 14(1), pages 1-24, December.
    8. Simon A Overduin & Philip Servos, 2008. "Symmetric Sensorimotor Somatotopy," PLOS ONE, Public Library of Science, vol. 3(1), pages 1-6, January.
    9. Amrita Pal & Jennifer A Ogren & Ravi S Aysola & Rajesh Kumar & Luke A Henderson & Ronald M Harper & Paul M Macey, 2021. "Insular functional organization during handgrip in females and males with obstructive sleep apnea," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-22, February.
    10. Gavin Perry & Nathan W Taylor & Philippa C H Bothwell & Colette C Milbourn & Georgina Powell & Krish D Singh, 2020. "The gamma response to colour hue in humans: Evidence from MEG," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-21, December.
    11. Olsen, Carmen & Gold, Anna, 2018. "Future research directions at the intersection between cognitive neuroscience research and auditors’ professional skepticism," Journal of Accounting Literature, Elsevier, vol. 41(C), pages 127-141.
    12. Ujwal Chaudhary & Bin Xia & Stefano Silvoni & Leonardo G Cohen & Niels Birbaumer, 2017. "Brain–Computer Interface–Based Communication in the Completely Locked-In State," PLOS Biology, Public Library of Science, vol. 15(1), pages 1-25, January.
    13. Chaogan Yan & Dongqiang Liu & Yong He & Qihong Zou & Chaozhe Zhu & Xinian Zuo & Xiangyu Long & Yufeng Zang, 2009. "Spontaneous Brain Activity in the Default Mode Network Is Sensitive to Different Resting-State Conditions with Limited Cognitive Load," PLOS ONE, Public Library of Science, vol. 4(5), pages 1-11, May.
    14. Laurens Winkelmeier & Carla Filosa & Renée Hartig & Max Scheller & Markus Sack & Jonathan R. Reinwald & Robert Becker & David Wolf & Martin Fungisai Gerchen & Alexander Sartorius & Andreas Meyer-Linde, 2022. "Striatal hub of dynamic and stabilized prediction coding in forebrain networks for olfactory reinforcement learning," Nature Communications, Nature, vol. 13(1), pages 1-21, December.
    15. Ani Eloyan & Shanshan Li & John Muschelli & Jim J Pekar & Stewart H Mostofsky & Brian S Caffo, 2014. "Analytic Programming with fMRI Data: A Quick-Start Guide for Statisticians Using R," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-13, February.
    16. Ai-Ling Hsu & Kun-Hsien Chou & Yi-Ping Chao & Hsin-Ya Fan & Changwei W Wu & Jyh-Horng Chen, 2016. "Physiological Contribution in Spontaneous Oscillations: An Approximate Quality-Assurance Index for Resting-State fMRI Signals," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-18, February.
    17. Jacob A. Westerberg & Jeffrey D. Schall & Geoffrey F. Woodman & Alexander Maier, 2023. "Feedforward attentional selection in sensory cortex," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    18. Qingfang Liu & Yao Zhao & Sumedha Attanti & Joel L. Voss & Geoffrey Schoenbaum & Thorsten Kahnt, 2024. "Midbrain signaling of identity prediction errors depends on orbitofrontal cortex networks," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    19. Kim, Sang-Yoon & Lim, Woochang, 2015. "Effect of small-world connectivity on fast sparsely synchronized cortical rhythms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 109-123.
    20. Adrián Ponce-Alvarez & Biyu J He & Patric Hagmann & Gustavo Deco, 2015. "Task-Driven Activity Reduces the Cortical Activity Space of the Brain: Experiment and Whole-Brain Modeling," PLOS Computational Biology, Public Library of Science, vol. 11(8), pages 1-26, August.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0213197. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.