Anti-fatigue Performance in SSVEP-Based Visual Acuity Assessment: A Comparison of Six Stimulus Paradigms

Overview

Journal Front Hum Neurosci

Specialty Neurology

Date 2020 Aug 28

PMID 32848675

Citations 15

Authors

Xiaowei Zheng

Guanghua Xu

Yubin Zhang

Renghao Liang

Kai Zhang

Yuhui Du

Jun Xie

Sicong Zhang

Affiliations

Soon will be listed here.

Abstract

Purpose: The occurrence of mental fatigue when users stare at stimuli is a critical problem in the implementation of steady-state visual evoked potential (SSVEP)-based visual acuity assessment, which may weaken the SSVEP amplitude and signal-to-noise ratio (SNR) and subsequently affect the results of visual acuity assessment. This study aimed to explore the anti-fatigue performance of six stimulus paradigms (reverse vertical sinusoidal gratings, reverse horizontal sinusoidal gratings, reverse vertical square-wave gratings, brief-onset vertical sinusoidal gratings, reversal checkerboards, and oscillating expansion-contraction concentric rings) in SSVEP acuity assessment.

Methods: Based on four indices of α + θ index, pupil diameter, National Aeronautics and Space Administration Task Load Index (NASA-TLX), and amplitude and SNR of SSVEPs, this study quantitatively evaluated mental fatigue in six SSVEP visual attention runs corresponding to six paradigms with 12 subjects.

Results: These indices of mental fatigue showed a good agreement. The results showed that the paradigm of motion expansion-contraction concentric rings had a superior anti-fatigue efficacy than the other five paradigms of conventional onset mode or pattern reversal mode during prolonged SSVEP experiment. The paradigm of brief-onset mode showed the lowest anti-fatigue efficacy, and the other paradigms of pattern reversal SSVEP paradigms showed a similar anti-fatigue efficacy, which was between motion expansion-contraction mode and onset mode.

Conclusion: This study recommended the paradigm of oscillating expansion-contraction concentric rings as the stimulation paradigm in SSVEP visual acuity because of its superior anti-fatigue efficacy.

Citing Articles

A subjective and objective fusion visual fatigue assessment system for different hardware and software parameters in SSVEP-based BCI applications.

Tian P, Xu G, Han C, Du C, Li H, Chen R Sci Rep. 2024; 14(1):27872.

PMID: 39537730 PMC: 11560949. DOI: 10.1038/s41598-024-79401-3.

SSVEP-based visual acuity threshold estimation: linear extrapolation to noise level baseline with various noise definition criteria.

Zheng X, Wei X, Xu G, Zhang R Cogn Neurodyn. 2024; 18(4):1641-1650.

PMID: 39104705 PMC: 11297861. DOI: 10.1007/s11571-023-10036-2.

Assessment of visual fatigue in SSVEP-based brain-computer interface: a comprehensive study.

Diez P, Orosco L, Garces Correa A, Carmona L Med Biol Eng Comput. 2024; 62(5):1475-1490.

PMID: 38267740 DOI: 10.1007/s11517-023-03000-z.

A quantization algorithm of visual fatigue based on underdamped second order stochastic resonance for steady state visual evoked potentials.

Tian P, Xu G, Han C, Zhang X, Zheng X, Wei F Front Neurosci. 2023; 17:1278652.

PMID: 38075275 PMC: 10702533. DOI: 10.3389/fnins.2023.1278652.

Fatigue factors and fatigue indices in SSVEP-based brain-computer interfaces: a systematic review and meta-analysis.

Azadi Moghadam M, Maleki A Front Hum Neurosci. 2023; 17:1248474.

PMID: 38053651 PMC: 10694510. DOI: 10.3389/fnhum.2023.1248474.

References

Li J, Sampson G, Vidyasagar T . Interactions between luminance and colour channels in visual search and their relationship to parallel neural channels in vision. Exp Brain Res. 2006; 176(3):510-8. DOI: 10.1007/s00221-006-0773-3. View

Hamilton R, Bach M, Heinrich S, Hoffmann M, Odom J, McCulloch D . VEP estimation of visual acuity: a systematic review. Doc Ophthalmol. 2020; 142(1):25-74. PMC: 7907051. DOI: 10.1007/s10633-020-09770-3. View

Won D, Hwang H, Dahne S, Muller K, Lee S . Effect of higher frequency on the classification of steady-state visual evoked potentials. J Neural Eng. 2015; 13(1):016014. DOI: 10.1088/1741-2560/13/1/016014. View

Norcia A, Tyler C . Spatial frequency sweep VEP: visual acuity during the first year of life. Vision Res. 1985; 25(10):1399-408. DOI: 10.1016/0042-6989(85)90217-2. View

Tyler C, Apkarian P, Levi D, Nakayama K . Rapid assessment of visual function: an electronic sweep technique for the pattern visual evoked potential. Invest Ophthalmol Vis Sci. 1979; 18(7):703-13. View

. Guideline 5: guidelines for standard electrode position nomenclature. Am J Electroneurodiagnostic Technol. 2006; 46(3):222-5. View

Laycock R, Crewther D, Crewther S . The advantage in being magnocellular: a few more remarks on attention and the magnocellular system. Neurosci Biobehav Rev. 2008; 32(8):1409-15. DOI: 10.1016/j.neubiorev.2008.04.008. View

Zheng X, Xu G, Wu Y, Wang Y, Du C, Wu Y . Comparison of the performance of six stimulus paradigms in visual acuity assessment based on steady-state visual evoked potentials. Doc Ophthalmol. 2020; 141(3):237-251. DOI: 10.1007/s10633-020-09768-x. View

Xie J, Xu G, Wang J, Zhang F, Zhang Y . Steady-state motion visual evoked potentials produced by oscillating Newton's rings: implications for brain-computer interfaces. PLoS One. 2012; 7(6):e39707. PMC: 3378577. DOI: 10.1371/journal.pone.0039707. View

10.

Yeshurun Y, Sabo G . Differential effects of transient attention on inferred parvocellular and magnocellular processing. Vision Res. 2012; 74:21-9. DOI: 10.1016/j.visres.2012.06.006. View

11.

Klimesch W . EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res Brain Res Rev. 1999; 29(2-3):169-95. DOI: 10.1016/s0165-0173(98)00056-3. View

12.

Zheng X, Xu G, Wang Y, Han C, Du C, Yan W . Objective and quantitative assessment of visual acuity and contrast sensitivity based on steady-state motion visual evoked potentials using concentric-ring paradigm. Doc Ophthalmol. 2019; 139(2):123-136. DOI: 10.1007/s10633-019-09702-w. View

13.

Wu Z, Yao D, Tang Y, Huang Y, Su S . Amplitude modulation of steady-state visual evoked potentials by event-related potentials in a working memory task. J Biol Phys. 2009; 36(3):261-71. PMC: 2868973. DOI: 10.1007/s10867-009-9181-9. View

14.

Bin G, Gao X, Yan Z, Hong B, Gao S . An online multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis method. J Neural Eng. 2009; 6(4):046002. DOI: 10.1088/1741-2560/6/4/046002. View

15.

Norcia A, Appelbaum L, Ales J, Cottereau B, Rossion B . The steady-state visual evoked potential in vision research: A review. J Vis. 2015; 15(6):4. PMC: 4581566. DOI: 10.1167/15.6.4. View

16.

Lee P, Sie J, Liu Y, Wu C, Ming-Huan Lee , Shu C . An SSVEP-actuated brain computer interface using phase-tagged flickering sequences: a cursor system. Ann Biomed Eng. 2010; 38(7):2383-97. DOI: 10.1007/s10439-010-9964-y. View

17.

Yan W, Xu G, Chen L, Zheng X . Steady-State Motion Visual Evoked Potential (SSMVEP) Enhancement Method Based on Time-Frequency Image Fusion. Comput Intell Neurosci. 2019; 2019:9439407. PMC: 6556311. DOI: 10.1155/2019/9439407. View

18.

Han C, Xu G, Xie J, Chen C, Zhang S . Highly Interactive Brain-Computer Interface Based on Flicker-Free Steady-State Motion Visual Evoked Potential. Sci Rep. 2018; 8(1):5835. PMC: 5895715. DOI: 10.1038/s41598-018-24008-8. View

19.

Tobimatsu S, Kato M . Effect of spatial frequency on transient and steady-state VEPs: stimulation with checkerboard, square-wave grating and sinusoidal grating patterns. J Neurol Sci. 1993; 118(1):17-24. DOI: 10.1016/0022-510x(93)90239-u. View

20.

Chen X, Wang Y, Nakanishi M, Gao X, Jung T, Gao S . High-speed spelling with a noninvasive brain-computer interface. Proc Natl Acad Sci U S A. 2015; 112(44):E6058-67. PMC: 4640776. DOI: 10.1073/pnas.1508080112. View