Using Semi-Supervised Domain Adaptation to Enhance EEG-Based Cross-Task Mental Workload Classification Performance

• Published in: IEEE journal of biomedical and health informatics Vol. 28; no. 12; pp. 7032 - 7039
• Main Authors: Wang, Tao, Ke, Yufeng, Huang, Yichao, He, Feng, Zhong, Wenxiao, Liu, Shuang, Ming, Dong
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2024
• Subjects: Adult; Algorithms; Brain modeling; Brain-Computer Interfaces; Calibration; cross-subject classification; cross-task classification; EEG; Electrodes; Electroencephalography; Electroencephalography - methods; Female; Humans; Male; Mental workload; Optimization; Signal Processing, Computer-Assisted; Task analysis; Task Performance and Analysis; Transfer learning; Workload - classification; Young Adult
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2024.3452410

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.