Using Semi-Supervised Domain Adaptation to Enhance EEG-Based Cross-Task Mental Workload Classification Performance
Tao Wang, Yufeng Ke, Yichao Huang, Feng He, Wenxiao Zhong, Shuang Liu, Dong Ming
Abstract
Mental workload (MWL) assessment is critical for accident prevention and operator safety. However, achieving cross-task generalization of MWL classification models is a significant challenge for real-world applications. Classifiers trained on labeled samples from one task often experience a notable performance drop when directly applied to samples from other tasks, limiting its use cases. To address this issue, we propose a semi-supervised cross-task domain adaptation (SCDA) method using power spectral density (PSD) features for MWL recognition across tasks (MATB-II and n-back). Our results demonstrated that the SCDA method achieved the best cross-task classification performance on our data and COG-BCI public dataset, with accuracies of 90.98% ± 9.36% and 96.61% ± 4.35%, respectively. Furthermore, in the cross-task classification of cross-subject scenarios, SCDA showed the highest average accuracy (75.39% ± 9.56% on our data, 90.98% ± 9.36% on the COG-BCI public dataset). The findings indicate that the semi-supervised transfer learning approach using PSD features is feasible and effective for cross-task MWL assessment.