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- Kim, Sung-Phil
- Brain-Computer Interface Lab.
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| DC Field | Value | Language |
|---|---|---|
| dc.citation.conferencePlace | KO | - |
| dc.citation.conferencePlace | Jeju Island | - |
| dc.citation.title | 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society | - |
| dc.contributor.author | Kang, Jae-Hwan | - |
| dc.contributor.author | Jo, Young Chang | - |
| dc.contributor.author | Kim, Sung-Phil | - |
| dc.date.accessioned | 2023-12-19T18:37:39Z | - |
| dc.date.available | 2023-12-19T18:37:39Z | - |
| dc.date.created | 2017-12-11 | - |
| dc.date.issued | 2017-07-14 | - |
| dc.description.abstract | To specify reliable features for advanced electrencephalography (EEG) based biometrics, we extracted the power and network parameters (Eigenvector centrality: EC) using two types of phase coupling (mean phase coherence (MPC) and phase lag index (PLI)) in each of 4 distinct frequency bands. A Euclidean distance-based authentication algorithm demonstrated that the MPC of high (>20 Hz) frequency rhythms yielded the best performance (accuracy of 95.4%) and the equal error rate was 8.1%, and the accuracy rate was 97.8% when fusing all the features. | - |
| dc.identifier.bibliographicCitation | 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society | - |
| dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/38898 | - |
| dc.language | 영어 | - |
| dc.publisher | IEEE EMBS | - |
| dc.title | Evaluation of EEG Characteristics for Personal Authentication | - |
| dc.type | Conference Paper | - |
| dc.date.conferenceDate | 2017-07-11 | - |
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