Individual alpha peak frequency based features for subject dependent EEG workload classification
The individual alpha peak frequency (IAPF) is an important biological indicator in Electroencephalogram (EEG) studies, with many research publications linking it to various cognitive functions. In this paper, we propose novel Power Spectral Density (PSD) alpha features based on IAPF to classify 2 an...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/146013 |
| _version_ | 1826113647772958720 |
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| author | Lim, Wei Lun Sourina, Olga Wang, Lipo Liu, Yisi |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Lim, Wei Lun Sourina, Olga Wang, Lipo Liu, Yisi |
| author_sort | Lim, Wei Lun |
| collection | NTU |
| description | The individual alpha peak frequency (IAPF) is an important biological indicator in Electroencephalogram (EEG) studies, with many research publications linking it to various cognitive functions. In this paper, we propose novel Power Spectral Density (PSD) alpha features based on IAPF to classify 2 and 4 levels of EEG multitasking workload data. When optimized IAPF was considered, a 1.55% and 1.56% increase in average accuracy for 48 subjects' data, with 35 and 33 subjects showing improvement was observed for 2 and 4 class cases respectively. This trend suggests that individual specific features are able to improve classification performance compared to generalized features for subject dependent cases. The proposed features, which incorporates the biological meaning of the IAPF and provides subject specific information, can be considered as a viable alternative to the general alpha power feature when designing novel subject dependent feature sets for BCI workload recognition applications. |
| first_indexed | 2024-10-01T03:26:39Z |
| format | Conference Paper |
| id | ntu-10356/146013 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:26:39Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | ntu-10356/1460132021-01-23T20:11:07Z Individual alpha peak frequency based features for subject dependent EEG workload classification Lim, Wei Lun Sourina, Olga Wang, Lipo Liu, Yisi School of Electrical and Electronic Engineering 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Fraunhofer Singapore Engineering::Electrical and electronic engineering Electroencephalography (EEG) Workload Classification The individual alpha peak frequency (IAPF) is an important biological indicator in Electroencephalogram (EEG) studies, with many research publications linking it to various cognitive functions. In this paper, we propose novel Power Spectral Density (PSD) alpha features based on IAPF to classify 2 and 4 levels of EEG multitasking workload data. When optimized IAPF was considered, a 1.55% and 1.56% increase in average accuracy for 48 subjects' data, with 35 and 33 subjects showing improvement was observed for 2 and 4 class cases respectively. This trend suggests that individual specific features are able to improve classification performance compared to generalized features for subject dependent cases. The proposed features, which incorporates the biological meaning of the IAPF and provides subject specific information, can be considered as a viable alternative to the general alpha power feature when designing novel subject dependent feature sets for BCI workload recognition applications. 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. 2021-01-21T03:05:12Z 2021-01-21T03:05:12Z 2016 Conference Paper Lim, W. L., Sourina, O., Wang, L., & Liu, Y. (2016). Individual alpha peak frequency based features for subject dependent EEG workload classification. Proceedings of 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC). doi:10.1109/SMC.2016.7844748 https://hdl.handle.net/10356/146013 10.1109/SMC.2016.7844748 en © 2016 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/SMC.2016.7844748 application/pdf |
| spellingShingle | Engineering::Electrical and electronic engineering Electroencephalography (EEG) Workload Classification Lim, Wei Lun Sourina, Olga Wang, Lipo Liu, Yisi Individual alpha peak frequency based features for subject dependent EEG workload classification |
| title | Individual alpha peak frequency based features for subject dependent EEG workload classification |
| title_full | Individual alpha peak frequency based features for subject dependent EEG workload classification |
| title_fullStr | Individual alpha peak frequency based features for subject dependent EEG workload classification |
| title_full_unstemmed | Individual alpha peak frequency based features for subject dependent EEG workload classification |
| title_short | Individual alpha peak frequency based features for subject dependent EEG workload classification |
| title_sort | individual alpha peak frequency based features for subject dependent eeg workload classification |
| topic | Engineering::Electrical and electronic engineering Electroencephalography (EEG) Workload Classification |
| url | https://hdl.handle.net/10356/146013 |
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