Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/145993
Title: Cross dataset workload classification using encoded wavelet decomposition features
Authors: Lim, Wei Lun
Sourina, Olga
Wang, Lipo
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: Lim, W. L., Sourina, O., & Wang, L. (2018). Cross dataset workload classification using encoded wavelet decomposition features. Proceedings of the International Conference on Cyberworlds, 300-303. doi:10.1109/CW.2018.00062
Abstract: 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.
URI: https://hdl.handle.net/10356/145993
ISBN: 9781538673157
DOI: 10.1109/CW.2018.00062
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/CW.2018.00062
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:Fraunhofer Singapore Conference Papers

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