Name
Abstract | ||
|---|---|---|
Electroencephalogram (EEG) recordings of brain waves have been shown to have unique pattern for each individual and thus have potential for biometric applications. In this paper, we propose an EEG feature extraction and hashing approach for person authentication. Multi-variate autoregressive (mAR) coefficients are extracted as features from multiple EEG channels and then hashed by using our recently proposed Fast Johnson-Lindenstrauss Transform (FJLT)-based hashing algorithm to obtain compact hash vectors. Based on the EEG hash vectors, a Naive Bayes probabilistic model is employed for person authentication. Our EEG hashing approach presents a fundamental departure from existing methods in EEG-biometry study. The promising results suggest that hashing may open new research directions and applications in the emerging EEG-based biometry area. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICASSP.2009.4959866 | ICASSP |
Keywords | Field | DocType |
eeg-based biometry area,eeg hash vector,compact hash vector,eeg data,mar coefficient,biometric application,multi-variate autoregressive,eeg feature extraction,person authentication,multiple eeg channel,fast johnson-lindenstrauss transform,eeg-biometry study,feature extraction,naive bayes,dimension reduction,security,fingerprint recognition,cryptography,hashing algorithm,probabilistic algorithm,predictive models,access control,authentication,data mining,hashing,electroencephalography,biometrics,robustness,probabilistic model | Authentication,Dimensionality reduction,Pattern recognition,Naive Bayes classifier,Fingerprint recognition,Computer science,Feature hashing,Feature extraction,Speech recognition,Artificial intelligence,Hash function,Biometrics | Conference |
ISSN | Citations | PageRank |
1520-6149 | 9 | 0.94 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chen He | 1 | 100 | 11.38 |
| Xudong Lv | 2 | 92 | 5.82 |
| Z. Jane Wang | 3 | 9 | 0.94 |