Mental workload detection and assessment through statistical features extraction and optimization using GEL-RF method for EEG signals using N-back dataset

Mental workload (MW) assessment measures the cognitive effort required to perform tasks and is crucial in fields such as aviation, clinical medicine, and healthcare. This study addresses the critical need to optimize cognitive load and resource allocation by evaluating MW through EEG-based analysis. Recently, MW assessment using EEG signals has gained importance, as they show a high correlation with specific cognitive processes and MW. Although previous studies have employed various techniques for MW assessment using EEG, detailed statistical feature selection remains underexplored. This work analyzes the N-back dataset, a standard benchmark for cognitive load, using four combinations of binary and multiclass classification to assess three MW levels. We propose a novel model, which integrates genetic, evolutionary and linear (GEL) feature selection techniques with a Random Forest (RF) classifier as GEL-RF model. Experimental results demonstrate that the GEL-RF model achieves classification accuracies of 97% for binary tasks and 96.3% for multiclass tasks MW assessment, outperforming existing methods. These results shows that our proposed model can improve MW assessment accuracy, helping to increase safety and efficiency in demanding mental tasks.