Cross dataset workload classification using encoded wavelet decomposition features
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...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2021
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| Online Access: | https://hdl.handle.net/10356/145993 |
| _version_ | 1824457106956222464 |
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| author | Lim, Wei Lun Sourina, Olga Wang, Lipo |
| author2 | 2018 International Conference on Cyberworlds (CW) |
| author_facet | 2018 International Conference on Cyberworlds (CW) Lim, Wei Lun Sourina, Olga Wang, Lipo |
| author_sort | Lim, Wei Lun |
| collection | NTU |
| description | 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. |
| first_indexed | 2025-02-19T04:04:44Z |
| format | Conference Paper |
| id | ntu-10356/145993 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T04:04:44Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | ntu-10356/1459932021-01-23T20:11:24Z Cross dataset workload classification using encoded wavelet decomposition features Lim, Wei Lun Sourina, Olga Wang, Lipo 2018 International Conference on Cyberworlds (CW) Fraunhofer Singapore Engineering::Electrical and electronic engineering Machine Learning Neurons 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. National Research Foundation (NRF) Accepted version This research is supported by the National Research Foundation, Singapore under its International Research Centres in Singapore Funding Initiative. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore. 2021-01-20T01:52:35Z 2021-01-20T01:52:35Z 2018 Conference Paper 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 9781538673157 https://hdl.handle.net/10356/145993 10.1109/CW.2018.00062 2-s2.0-85061448610 300 303 en © 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 application/pdf |
| spellingShingle | Engineering::Electrical and electronic engineering Machine Learning Neurons Lim, Wei Lun Sourina, Olga Wang, Lipo Cross dataset workload classification using encoded wavelet decomposition features |
| title | Cross dataset workload classification using encoded wavelet decomposition features |
| title_full | Cross dataset workload classification using encoded wavelet decomposition features |
| title_fullStr | Cross dataset workload classification using encoded wavelet decomposition features |
| title_full_unstemmed | Cross dataset workload classification using encoded wavelet decomposition features |
| title_short | Cross dataset workload classification using encoded wavelet decomposition features |
| title_sort | cross dataset workload classification using encoded wavelet decomposition features |
| topic | Engineering::Electrical and electronic engineering Machine Learning Neurons |
| url | https://hdl.handle.net/10356/145993 |
| work_keys_str_mv | AT limweilun crossdatasetworkloadclassificationusingencodedwaveletdecompositionfeatures AT sourinaolga crossdatasetworkloadclassificationusingencodedwaveletdecompositionfeatures AT wanglipo crossdatasetworkloadclassificationusingencodedwaveletdecompositionfeatures |