Enhancing Mental Workload Prediction through LightGBM during Multitasking

Multitasking is an essential aspect of daily life; however, it significantly increases mental workload (MWL), which can affect cognitive performance, decision making, and overall effectiveness. Thus, accurately assessing MWL is significant in various fields, including human-computer interaction, aviation, and healthcare, where cognitive overload can lead to unsuitable decisions. The brain computer interface (BCI) based on electroencephalography (EEG) presents a viable, non-invasive option for real-time monitoring of MWL, allowing an adaptive system to improve performance and user experience. However, because EEG patterns vary widely among individuals, it is still challenging to develop a generalized MWL prediction model. Therefore, Light Gradient Boosting Machine (LightGBM) with manually extracted features is proposed. Our analysis was based on the "STEW"dataset, which includes two task conditions: "No task"and a multitasking activity using the SIMKAP framework. The proposed model achieved an average accuracy of 84.0% (±14.4%) and an average F1-score of 83.1% (±18.2%), showcasing its strong predictive performance while maintaining computational efficiency compared to deep learning methods. These results highlight LightGBM's potential as a fast, subject-independent MWL classification tool, therefore enabling the design of scalable and flexible cognitive monitoring systems for practical use.