A Comparative Analysis of Deep Learning and Traditional Machine Learning for Classifying Cognitive Workload from Raw EEG Signals

The objective assessment of cognitive workload is critical for enhancing performance and safety in high-stakes environments such as aviation and process control. This study presents a comparative analysis of two machine learning paradigms for classifying cognitive workload into three distinct levels (Low, Moderate, High) using electroencephalography (EEG). We developed and evaluated a deep learning model based on a 1D Convolutional Neural Network (CNN) that processes raw time-series EEG data, and compared it against a traditional machine learning baseline, a Random Forest (RF) classifier, trained on hand-engineered statistical features. The CNN model achieved a superior test accuracy of 94.2%, significantly outperforming the Random Forest model, which achieved an accuracy of 62.0%. This 32.2% performance gap strongly indicates that the raw temporal structure of EEG signals contains discriminative features for workload classification that are not captured by standard statistical summaries. The results validate the efficacy of deep learning for automated feature extraction in neurophysiological data and provide a robust, deployable model for real-time cognitive workload monitoring systems.