Mental workload assessment using deep learning models from EEG Signals: a systematic review

Mental workload (MWL) assessment is crucial in information systems (IS), impacting task performance, user experience, and system effectiveness. Deep learning offers promising techniques for MWL classification using electroencephalography (EEG), which monitors cognitive states dynamically and unobtrusively. Our research explores deep learning's potential and challenges in EEG-based MWL classification, focusing on training inputs, cross-validation methods, and classification problem types. We identify five types of EEG-based MWL classification: within-subject, cross-subject, cross-session, cross-task, and combined cross-task and -subject. Success depends on managing dataset uniqueness, session and task variability, and artifact removal. Despite potential, real-world applications are limited. Enhancements are necessary in self-reporting methods, universal preprocessing standards, and MWL assessment accuracy. Specifically, inaccuracies are inflated when data is shuffled before splitting to train and test sets, disrupting EEG signals’ temporal sequence. In contrast, methods like the time-series cross-validation or leave-session-out approach better preserve temporal integrity, offering more accurate model performance evaluations. Utilizing deep learning for EEG-based MWL assessment could significantly improve IS functionality and adaptability in real-time based on user cognitive states.