Lim, Wei LunWei LunLimSourina, OlgaOlgaSourinaWang, LipoLipoWang2022-03-142022-03-142018https://publica.fraunhofer.de/handle/publica/40716310.1109/CW.2018.00062For 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.enLead Topic: Digitized WorkResearch Line: Human computer interaction (HCI)feature extractionbrain-computer interfaces (BCI)wavelet transformationconference paper