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<p>Cognitive workload (CW) refers to the mental effort required to perform a task and is critical to monitor in high-stakes environments such as robot-assisted surgery (RAS), where excessive demand can impair decision-making and performance. This study proposes a machine learning framework to estimate CW using electroencephalography (EEG) signals, focusing on four cortical regions: frontal, temporal, parietal, and occipital. EEG epochs were processed to extract both time-domain features (mean, variance, skewness, kurtosis, RMS, zero-crossings) and frequency-domain features (power spectral density across delta, theta, alpha, beta, and gamma bands). To enhance computational efficiency, data were downsampled from 500 to 128&#xa0;Hz, with minimal signal degradation confirmed via topographic and spectrogram-based comparisons. Random Forest Regressor (RFR) was trained to predict region-specific EEG-derived CW scores, achieving high accuracy with R<sup>2</sup> (coefficient of determination) values of 0.9947 (temporal), 0.9692 (parietal), 0.9635 (occipital), and 0.9329 (frontal), alongside low RMSE scores. Feature importance analysis identified kurtosis, RMS, and select power bands as key predictors. Model robustness was validated using tenfold cross-validation and statistical significance testing (p &lt; 0.0001). Comparative evaluation with SVR, Linear Regression, and XGBoost confirmed the superior generalizability of the RFR model. Topographic EEG maps and time–frequency spectrograms visually supported region-specific activation patterns, reinforcing the effectiveness of spatially localized workload modeling. These findings demonstrate a promising, interpretable, and high-performing pipeline for EEG-based cognitive workload estimation, with broad implications for adaptive neuroergonomic systems in surgical and clinical settings.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月6日

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  • Mohammed Atheef G A,
  • Omkar S Powar

摘要

Cognitive workload (CW) refers to the mental effort required to perform a task and is critical to monitor in high-stakes environments such as robot-assisted surgery (RAS), where excessive demand can impair decision-making and performance. This study proposes a machine learning framework to estimate CW using electroencephalography (EEG) signals, focusing on four cortical regions: frontal, temporal, parietal, and occipital. EEG epochs were processed to extract both time-domain features (mean, variance, skewness, kurtosis, RMS, zero-crossings) and frequency-domain features (power spectral density across delta, theta, alpha, beta, and gamma bands). To enhance computational efficiency, data were downsampled from 500 to 128 Hz, with minimal signal degradation confirmed via topographic and spectrogram-based comparisons. Random Forest Regressor (RFR) was trained to predict region-specific EEG-derived CW scores, achieving high accuracy with R2 (coefficient of determination) values of 0.9947 (temporal), 0.9692 (parietal), 0.9635 (occipital), and 0.9329 (frontal), alongside low RMSE scores. Feature importance analysis identified kurtosis, RMS, and select power bands as key predictors. Model robustness was validated using tenfold cross-validation and statistical significance testing (p < 0.0001). Comparative evaluation with SVR, Linear Regression, and XGBoost confirmed the superior generalizability of the RFR model. Topographic EEG maps and time–frequency spectrograms visually supported region-specific activation patterns, reinforcing the effectiveness of spatially localized workload modeling. These findings demonstrate a promising, interpretable, and high-performing pipeline for EEG-based cognitive workload estimation, with broad implications for adaptive neuroergonomic systems in surgical and clinical settings.