Paper Info
Title
Improved Time-Frequency Features and Electrode Placement for EEG-Based Biometric Person Recognition
Abstract
This paper introduces a novel feature extraction method for biometric recognition using EEG data and provides an analysis of the impact of electrode placements on performance. The feature extraction method is based on the wavelet transform of the raw EEG signal. Furthermore, the logarithms of wavelet coefficients are processed using the discrete cosine transform (DCT). The DCT coefficients from each wavelet band are used to form the feature vectors for classification. As an application in the biometrics scenario, the effectiveness of the electrode locations on person recognition is also investigated, and suggestions are made for electrode positioning to improve performance. The effectiveness of the proposed feature was investigated in both identification and verification scenarios. The identification results of 98.24% and 93.28% were obtained using the EEG Motor movement/imagery dataset (MM/I) and the UCI EEG database dataset, respectively, which compares favorably with other published reports while using a significantly smaller number of electrodes. The performance of the proposed system also showed substantial improvements in the verification scenario, when compared with some similar systems from the published literature. A multi-session analysis is simulated using with eyes open and eyes closed recordings from the MM/I database. It is found that the proposed feature is less influenced by time separation between training and testing compared with a conventional feature based on power spectral analysis.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2910752
IEEE ACCESS
Keywords
Field
DocType
Biometrics,feature extraction,EEG
Feature vector,Pattern recognition,Computer science,Discrete cosine transform,Feature extraction,Time–frequency analysis,Artificial intelligence,Logarithm,Biometrics,Wavelet,Wavelet transform,Distributed computing
Journal
Volume
ISSN
Citations 
7
2169-3536
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Su Yang1156.25
Sanaul Hoque29313.16
Farzin Deravi329636.61