Effects of fatigue on steady state motion visual evoked potentials: Optimised stimulus parameters for a zoom...

Area of Science:

• Neuroscience
• Biomedical Engineering
• Human-Computer Interaction

Background:

• Brain-computer interfaces (BCIs) using steady-state visual evoked potentials (SSVEPs) suffer performance degradation with prolonged use due to visual fatigue.
• The zoom motion-based steady-state motion visual evoked potentials (SSMVEPs) paradigm was developed to mitigate this issue.
• Optimizing SSMVEP stimulation parameters is crucial for maintaining BCI performance and user comfort.

Purpose of the Study:

• To optimize stimulation parameters for the zoom motion-based SSMVEP paradigm.
• To reduce the decrease in detection accuracy caused by visual fatigue in SSMVEP BCIs.
• To identify SSMVEP parameters that enhance recognition accuracy and minimize fatigue.

Main Methods:

• Compared eight zoom motion-based SSMVEP paradigms with varied stimulation parameters (size, luminance, color, shape, frequency, refresh rate).
• Assessed recognition accuracy and evaluated visual fatigue using signal-to-noise ratio (SNR) of SSMVEP and EEG alpha/theta band power.
• Measured fatigue across four levels during stimulation tasks.

Main Results:

• All tested SSMVEP paradigms showed high accuracy.
• A smaller stimulus size paradigm achieved the highest accuracy (97.2%) compared to the standard (94.9%) and showed minimal accuracy reduction due to fatigue.
• The smaller stimulus SSMVEP paradigm demonstrated a lower decrease in SNR and alpha/theta ratio compared to the standard paradigm across fatigue levels.

Conclusions:

• Optimizing stimulation parameters for zoom motion-based SSMVEP BCIs can maintain high performance.
• Utilizing a smaller stimulus size in SSMVEP paradigms effectively reduces accuracy decline associated with visual fatigue.
• The reduced SNR and alpha/theta index changes in the smaller stimulus paradigm indicate its superiority in mitigating fatigue effects.