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- Kim, Sung-Phil
- Brain-Computer Interface Lab.
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Evaluation of EEG Characteristics for Personal Authentication
- Author(s)
- Kang, Jae-Hwan, Jo, Young Chang, Kim, Sung-Phil
- Issued Date
- 2017-07-14
- Citation
- 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
- 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.
- Publisher
- IEEE EMBS
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