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Deep-Mental Workload Intelligent System: An AI-Augmented System to Predict Employee Mental Workload Based on EEG Data Using Deep Learning
Abstract
Recently, the measurement of mental workload has emerged as a crucial aspect of well-being process in socio-technical organizations. Excessive mental workload reduces work productivity, whereas insufficient mental workload leads to the underutilization of human resources. Significant research utilizing machine learning algorithms, such as SVM and KNN, based on electroencephalogram (EEG) signals, has been used for stress classification in the past few years. However, issues with mental workload and classification accuracy persist across these studies. Therefore, this chapter proposes an AI-augmented web-based information system to assess mental workload by employing a deep learning model. The proposed deep learning algorithm is used for the classification and feature extraction from EEG signals to accurately classify mental workload status. The proposed AI-augmented system would become a significant tool for making more effective and precise predictions about employees' mental workload in organizations.
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