Name
Title | ||
|---|---|---|
Feature Extraction of Individual Differences for Identification Recognition Based on Resting EEG. |
Abstract | ||
|---|---|---|
Biometric recognition based on individual difference was commonly used in many aspects in life. Compared with the traditional features used in person identification, EEG-based biometry is an emerging research topic with high security and uniqueness, and it may open new research applications in the future. However, little work has been done within this area. In this paper, four feature extraction techniques were employed to characterize the resting EEG signals: AR model, time-domain power spectrum, frequency-domain power spectrum and phase locking value. In our experiments using 20 healthy subjects, the classification accuracy by support vector machine reached 90.52% with AR model parameters, highest of the four kinds of features. The results show the potential applications of resting EEG signal in person identification. © 2013 Springer-Verlag Berlin Heidelberg. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-39143-9-56 | HCI (20) |
Keywords | Field | DocType |
AR model,individual differences,person identification,resting EEG,support vector machine | Autoregressive model,Uniqueness,Pattern recognition,Computer science,Support vector machine,Feature extraction,Spectral density,Artificial intelligence,Biometrics,Electroencephalography,Phase locking | Conference |
Volume | Issue | ISSN |
8023 LNCS | PART 1 | 16113349 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rui Xu | 1 | 0 | 3.04 |
| Dong Ming | 2 | 105 | 51.47 |
| Yanru Bai | 3 | 6 | 2.49 |
| Jing Liu | 4 | 0 | 0.34 |
| Hongzhi Qi | 5 | 49 | 20.61 |
| Qiang Xu | 6 | 2165 | 135.87 |
| Peng Zhou | 7 | 65 | 9.65 |
| Lixin Zhang | 8 | 2 | 3.75 |
| Baikun Wan | 9 | 104 | 16.90 |