https://scholars.lib.ntu.edu.tw/handle/123456789/611225
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.author | AN-YEU(ANDY) WU | zz |
dc.creator | Liu P.-K.; Beh W.-K.; Shih C.-Y.; Chen Y.-T.; Wu A.-Y.A. | - |
dc.date.accessioned | 2022-05-19T07:46:37Z | - |
dc.date.available | 2022-05-19T07:46:37Z | - |
dc.date.issued | 2019 | - |
dc.identifier.isbn | 9.78151E+12 | - |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077052690&doi=10.1109%2fBIOCAS.2019.8919019&partnerID=40&md5=de693932db8eea6cf3e8f1cd719bea03 | - |
dc.identifier.uri | https://scholars.lib.ntu.edu.tw/handle/123456789/611225 | - |
dc.description.abstract | As the era of Brain-Computer Interfacing (BCI) arrives, computationally measuring human mental workload via Electroencephalography (EEG) signal has become a crucial research field. Conventionally, mental workload assessment studies are mainly based on time-statistics, frequency, and wavelet domain features. In this paper, we present a mental workload assessment system in discriminating high and low mental workload by extracting EEG features from two new domains: Time-complexity and entropy domains features. According to statistical analysis, the result demonstrates that the Frontal and Frontal-Central are two dominating regions. In addition, by fusing the traditional and new features, we boosted the classification performance from 69% to 88%. It indicates time-complexity and entropy domain features are able to extract some non-linear characteristics of EEG, which could not be achieved by traditional approaches. We conclude that the new features are feasible to assess human mental workload, and could provide complementary information to traditional features. © 2019 IEEE. | - |
dc.language | en_US | - |
dc.relation.ispartof | BioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings | - |
dc.subject | Complexity; EEG; Entropy; Mental Workload | - |
dc.subject.other | Brain computer interface; Electrophysiology; Entropy; Brain-computer interfacing; Classification performance; Complexity; Human mental workloads; Mental workload; Mental workload assessments; Nonlinear characteristics; Wavelet domain features; Electroencephalography | - |
dc.title | Entropy and Complexity Assisted EEG-based Mental Workload Assessment System | en_US |
dc.type | conference paper | en |
dc.identifier.doi | 10.1109/BIOCAS.2019.8919019 | - |
dc.identifier.scopus | 2-s2.0-85077052690 | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | none | - |
item.fulltext | no fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_5794 | - |
item.openairetype | conference paper | - |
crisitem.author.dept | Electrical Engineering | - |
crisitem.author.dept | Electronics Engineering | - |
crisitem.author.dept | Intel-NTU Connected Context Computing Center | - |
crisitem.author.dept | Center for Artificial Intelligence and Advanced Robotics | - |
crisitem.author.orcid | 0000-0003-4731-8633 | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
crisitem.author.parentorg | Others: University-Level Research Centers | - |
crisitem.author.parentorg | Others: International Research Centers | - |
crisitem.author.parentorg | Others: University-Level Research Centers | - |
顯示於: | 電機工程學系 |
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