TY - GEN
T1 - A Study of Combined Lossy Compression and Person Identification on EEG Signals
AU - Nguyen, Binh
AU - Ma, Wanli
AU - Tran, Dat
PY - 2018/6/7
Y1 - 2018/6/7
N2 - Biometric information extracted from electroencephalogram (EEG) signals is being used increasingly in person identification systems thanks to several advantages, compared to traditional ones such as fingerprint, face and voice. However, one of the major challenges is that a huge amount of EEG data needs to be processed, transmitted and stored. The use of EEG compression is therefore becoming necessary. Although the lossy compression technique gives a higher Compression Ratio (CR) than lossless ones, they introduce the loss of information in recovered signals, which may affect to the performance of EEG-based person identification systems. In this paper, we investigate the impact of lossy compression on EEG data used in EEG-based person identification systems. Experimental results demonstrate that in the best case, CR could achieve up to 70 with minimal loss of person identification performance, and using EEG lossy compression is feasible compared to using lossless one.
AB - Biometric information extracted from electroencephalogram (EEG) signals is being used increasingly in person identification systems thanks to several advantages, compared to traditional ones such as fingerprint, face and voice. However, one of the major challenges is that a huge amount of EEG data needs to be processed, transmitted and stored. The use of EEG compression is therefore becoming necessary. Although the lossy compression technique gives a higher Compression Ratio (CR) than lossless ones, they introduce the loss of information in recovered signals, which may affect to the performance of EEG-based person identification systems. In this paper, we investigate the impact of lossy compression on EEG data used in EEG-based person identification systems. Experimental results demonstrate that in the best case, CR could achieve up to 70 with minimal loss of person identification performance, and using EEG lossy compression is feasible compared to using lossless one.
KW - Biometric information
KW - DWT-AAC
KW - EEG lossy compression
KW - EEG-based person identification
KW - SPIHT
UR - http://www.scopus.com/inward/record.url?scp=85048629122&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-94120-2_43
DO - 10.1007/978-3-319-94120-2_43
M3 - Conference contribution
AN - SCOPUS:85048629122
SN - 9783319941196
VL - 771
T3 - Advances in Intelligent Systems and Computing
SP - 449
EP - 458
BT - International Joint Conference SOCO’18-CISIS’18-ICEUTE’18, San Sebastian, Spain, June 6-8 Proceedings
A2 - Grana, Manuel
A2 - Lopez-Guede, Jose Manuel
A2 - Etxaniz, Oier
A2 - Herrero, Alvaro
A2 - Saez, Jose Antonio
A2 - Quintian, Hector
A2 - Corchado, Emilio
PB - Springer
CY - Cham, Switzerland
T2 - International Joint Conference: 13th International Conference on Soft Computing Models, SOCO 2018, 11th International Conference on Computational Intelligence in Security for Information Systems, CISIS 2018 and 9th International Conference on EUropean Transnational Education, ICEUTE 2018
Y2 - 6 June 2018 through 8 June 2018
ER -