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

Abstract

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 briefonset 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 expansioncontraction concentric rings as the stimulation paradigm in SSVEP visual acuity because of its superior anti-fatigue efficacy.

Enhancing Performance of SSVEP-Based Visual Acuity via Spatial Filtering

“…Common average reference, a commonly used spatial filtering method, is achieved by subtracting the mean signals of all electrodes from the selected electrode signals ( Zheng et al, 2020d ). Here, also choosing the Oz electrode, the weights w can be expressed as:…”

“…On the basis of this idea, several methods of extracting optimal spatial filters to reconstruct source activities from scalp EEG signals have been carried out to enhance the SNR of SSVEPs. For instance, the basic spatial filtering methods [e.g., Laplacian combination ( Friman et al, 2007 ) and common average reference (CAR) ( Zheng et al, 2020d )] and the model-based spatial filtering methods [e.g., minimum energy combination (MEC) ( Friman et al, 2007 ), canonical correlation analysis (CCA) ( Bin et al, 2009 ; Zheng et al, 2020b ; Li et al, 2021 ), and multivariate synchronization index (MSI) ( Zhang et al, 2014a )] have been applied to improve the performance of SSVEPs. However, to date, little is known about whether there is an enhancement of the spatial filtering technique from multielectrode signals on SSVEP visual acuity.…”

Brain–Computer Interface Speller Based on Steady-State Visual Evoked Potential: A Review Focusing on the Stimulus Paradigm and Performance

“…Visual fatigue leads to poor user experience, and a worse user experience often means worse application prospects. Although some studies have given methods to evaluate user fatigue [ 8 , 120 , 121 ], no unified standard has been developed to date, and research on this issue is inadequate. The challenge faced by such research is that it is very difficult to quantitatively analyze the degree of fatigue, which often depends on the subjective feelings of the subjects.…”

“…However, the frequencies that can be used to evoke the SSVEP are limited by the refresh frequency of the screen. Another problem is that the SSVEP-based BCI speller induces visual fatigue in the user, where such fatigue is not severe but cannot be eliminated [ 8 ]. A performance comparison between the SSVEP-based BCI speller and the P300-based speller has yielded differences between LIS patients and healthy subjects [ 9 , 10 ].…”

Anti-Heartbeat-Evoked Potentials Performance in Event-Related Potentials-Based Mental Workload Assessment

“…The effect of noise on ERP analysis has been minimized thanks to the development of methods to remove noise in EEG signals. However, we hypothesized that other factors, such as changes in mental state (i.e., stress, emotion, and cognitive load) and evoked potentials [i.e., heartbeat-evoked potential (HEP)], in addition to noise, could affect the ERP signals, leading to a decrease in performance ( Zhang et al, 2020 ; Zheng et al, 2020 ). Some previous studies have sought to improve the classification performance by considering the changes in mental state ( Ko et al, 2020 ; Zhang et al, 2020 ), but no study using HEP has been reported.…”