EEGMeNet: End-to-End Multi-Task Neural Network for Brain-Based Mental Workload Classification

Abstract

High mental workload poses significant challenges across various domains, often impairing performance and decision-making. Recent advancements in Internet of Things (IoT) technologies have enabled continuous monitoring of brain activity via electroencephalography (EEG), facilitating real-time assessment of mental workload during task execution. Early detection of elevated workload levels is essential for mitigating potential adverse outcomes. This paper presents EEGMeNet, an end-to-end, multi-task neural network designed to classify mental workload levels from EEG signals. EEGMeNet consists of four key components: (1) a local feature learner that extracts spectral and spatial characteristics, (2) a global feature learner employing attention-based mechanisms to model temporal dependencies, (3) a feature preserver that enhances representation learning, and (4) a supervised classifier for mental workload classification. The proposed model is evaluated across five mental task EEG datasets - including one acquired in clinical settings - under both within-subject and cross-subject, as well as within-session and cross-session classification paradigms. EEGMeNet outperforms nine state-of-the-art baselines, achieving an accuracy of 87.49±2.66% and an F1 score of 85.26±2.63% in cross-subject evaluations, showing at least a 9% improvement in accuracy and a 12% gain in F1 score. Moreover, EEGMeNet demonstrates low prediction time complexity, making it well-suited for deployment on resource-constrained IoT devices. These results underscore EEGMeNet's robustness to inter-subject and multi-session EEG variability, validating its effectiveness for real-world EEG-IoT applications. This work establishes EEGMeNet as a promising foundation for future mental workload classification research in EEG-IoT environments.