Use of Sine Shaped High-Frequency Rhythmic Visual Stimuli Patterns for SSVEP Response Analysis and Fatigue Rate Evaluation in Normal Subjects

Abstract

Background: Recent EEG-SSVEP signal based BCI studies have used high frequency square pulse visual stimuli to reduce subjective fatigue. However, the effect of total harmonic distortion (THD) has not been considered. Compared to CRT and LCD monitors, LED screen displays high-frequency wave with better refresh rate. In this study, we present high frequency sine wave simple and rhythmic patterns with low THD rate by LED to analyze SSVEP responses and evaluate subjective fatigue in normal subjects.Materials and Methods: We used patterns of 3-sequence high-frequency sine waves (25, 30, and 35 Hz) to design our visual stimuli. Nine stimuli patterns, 3 simple (repetition of each of above 3 frequencies e.g., P25-25-25) and 6 rhythmic (all of the frequencies in 6 different sequences e.g., P25-30-35) were chosen. A hardware setup with low THD rate (<0.1%) was designed to present these patterns on LED. Twenty two normal subjects (aged 23–30 (25 ± 2.1) yrs) were enrolled. Visual analog scale (VAS) was used for subjective fatigue evaluation after presentation of each stimulus pattern. PSD, CCA, and LASSO methods were employed to analyze SSVEP responses. The data including SSVEP features and fatigue rate for different visual stimuli patterns were statistically evaluated.Results: All 9 visual stimuli patterns elicited SSVEP responses. Overall, obtained accuracy rates were 88.35% for PSD and > 90% for CCA and LASSO (for TWs > 1 s). High frequency rhythmic patterns group with low THD rate showed higher accuracy rate (99.24%) than simple patterns group (98.48%). Repeated measure ANOVA showed significant difference between rhythmic pattern features (P < 0.0005). Overall, there was no significant difference between the VAS of rhythmic [3.85 ± 2.13] compared to the simple patterns group [3.96 ± 2.21], (P = 0.63). Rhythmic group had lower within group VAS variation (min = P25-30-35 [2.90 ± 2.45], max = P35-25-30 [4.81 ± 2.65]) as well as least individual pattern VAS (P25-30-35).Discussion and Conclusion: Overall, rhythmic and simple pattern groups had higher and similar accuracy rates. Rhythmic stimuli patterns showed insignificantly lower fatigue rate than simple patterns. We conclude that both rhythmic and simple visual high frequency sine wave stimuli require further research for human subject SSVEP-BCI studies.

Introducing Rhythmic Sinusoidal Amplitude-Modulated Auditory Stimuli with Multiple Message Frequency Coding for Fatigue Reduction in Normal Subjects: An EEG Study

“…This is in agreement with the findings of our previous study on the comparison between the fatigue levels that simple and rhythmic single-message sinusoidal AM stimuli can cause (Shamsi et al, 2017). It is also in accordance with the results of previous studies on rhythmic (Keihani et al, 2018b;Keihani et al, 2018a) and chaotic (Shirzhiyan et al, 2019) visual stimuli that reduced the subjective fatigue, and confirms our second hypothesis. In addition, the insignificant and infinitesimal correlation between the fatigue and the psychological state can ensure us that the subjects truly reported the fatigue that was mainly caused by listening to the stimuli, regardless of their psychological status.…”

“…Moreover, rhythmic stimulation modulates the intrinsic neural oscillatory characteristics (Treder et al, 2014;Herrmann et al, 2016) and facilitates keeping focus (Sato et al, 2019). Interestingly, it was revealed that rhythmic (Keihani et al, 2018b;Keihani et al, 2018a) and chaotic (Shirzhiyan et al, 2019) visual alongside auditory stimuli (Shamsi et al, 2017) brought about both enough distinguishable EEGs and less subjects' fatigue, compared to simple stimuli. Thus, rhythmic stimuli are one of the promising options for being used in aBCI.…”

Toward New Modalities in VEP-Based BCI Applications Using Dynamical Stimuli: Introducing Quasi-Periodic and Chaotic VEP-Based BCI

“…The trigger output of g.USB Amp (start time of EEG recording) and the output of a Texas Instruments optical sensor (visual stimuli) were sent to National Instruments (NI) DAQ. Details of the signal recording setup are reported in our previous studies ( Keihani et al, 2018 ; Shirzhiyan et al, 2019 ).…”

“…Due to their high decoding accuracy, external stimuli such as periodic flickers are commonly used in VEP-based BCIs evoking steady-state visual evoked potentials (SSVEPs) ( Vialatte et al, 2010 ; Keihani et al, 2018 ). In SSVEP-based BCIs, the stimulus comprises a constant frequency that varies from low to high (1–100 Hz), which then leads to specific EEG responses that correlate with the stimulus frequency ( Vialatte et al, 2010 ).…”

“…These periodic patterns may also lead to migraine headache ( DeTommaso et al, 1999 ) or even epileptic seizures ( Fisher et al, 2005 ). Studies have used various methods including the use of high-frequency stimuli rather than lower frequencies ( Allison et al, 2010 ; Sakurada et al, 2015 ; Ajami et al, 2018 ), polychromatic stimuli ( Chien et al, 2017 ), motion Newton’s rings and motion checkerboards ( Xie et al, 2012 , 2017 ; Yan et al, 2017 ; Han et al, 2018 ), and rhythmic pattern stimuli ( Keihani et al, 2018 ) to minimize the subjective visual fatigue, which still remains an important problem in VEP-based BCI applications.…”