Cross dataset workload classification using encoded wavelet decomposition features

• Main Authors: Lim, Wei Lun, Sourina, Olga, Wang, Lipo
• Other Authors: 2018 International Conference on Cyberworlds (CW)
• Format: Conference or Workshop Item
• Language: English
• Published: 2021
• Subjects: Engineering::Electrical and electronic engineering; Machine Learning; Neurons
• Online Access: https://hdl.handle.net/10356/145993
• Institution: Nanyang Technological University

For practical applications, it is desirable for a trained classification system to be independent of task and/or subject. In this study, we show one-way transfer between two independent EEG workload datasets: from a large multitasking dataset with 48 subjects to a second Stroop test dataset with 18 subjects. This was achieved with a classification system trained using sparse encoded representations of the decomposed wavelets in the alpha, beta and theta power bands, which learnt a feature representation that outperformed benchmark power spectral density features by 3.5%. We also explore the possibility of enhancing performance with the utilization of domain adaptation techniques using transfer component analysis (TCA), obtaining 30.0% classification accuracy for a 4-class cross dataset problem.