Estimating cognitive workload in robot assisted surgery using time and frequency features from EEG epochs with random forest regression

Key Points

• Cognitive workload significantly correlates with specific features from EEG epochs, and these indicators can enhance surgical performance.
• Random forest regression achieves a correlation coefficient of 0.85, indicating strong predictive power for cognitive workload using EEG data.
• Assessment using time and frequency features from EEG signals during robot assisted surgery provides new insights into cognitive processes.
• These findings highlight the need for further validation in diverse surgical settings, suggesting external validation may improve applicability.