Effects of Distracting Task with Different Mental Workload on Steady-State Visual Evoked Potential Based Brain...

Area of Science:

• Neuroscience
• Human-Computer Interaction
• Biomedical Engineering

Background:

• Brain-computer interfaces (BCIs) are emerging as key technologies for neural information extraction.
• Steady-state visual evoked potential (SSVEP)-based BCIs offer high signal-to-noise ratio and information transfer rates.
• Current SSVEP-BCI systems typically require focused attention, excluding concurrent tasks.

Purpose of the Study:

• To investigate the impact of a distracting verbal n-back task with varying mental workload on SSVEP-BCI performance.
• To quantify the reduction in SSVEP-BCI accuracy under cognitive load.
• To explore the correlation between subjective mental workload and BCI performance.

Main Methods:

• Fifteen subjects participated in the study.
• SSVEP-BCI performance was evaluated while subjects performed a verbal n-back task.
• Mental workload was manipulated across different n-back levels (1-back to 4-back).
• Recognition accuracy and subjective mental workload were measured.

Main Results:

• SSVEP-BCI recognition accuracy was significantly reduced by the distracting n-back task.
• Performance degradation was most pronounced at higher mental workload levels (4-back).
• Average classification accuracy dropped by up to 8.67%.
• A significant negative correlation was found between accuracy and subjective mental workload (max r = -0.48, p < 0.001).

Conclusions:

• Distracting cognitive tasks, particularly those with high mental workload, pose a significant challenge to SSVEP-BCI performance.
• The findings suggest potential limitations for the widespread daily adoption of SSVEP-BCIs.
• Future research should focus on developing strategies to mitigate the effects of cognitive load on BCI systems.