Investigating the possibility of applying EEG lossy compression to EEG-based user authentication

The Binh NGUYEN, Dang Nguyen, Wanli Ma, Dat Tran

Research output: A Conference proceeding or a Chapter in BookConference contribution

8 Citations (Scopus)

Abstract

Using EEG signal as a new type of biometric in user authentication systems has been emerging as an interesting research topic. However, one of the major challenges is that a huge amount of EEG data that needs to be processed, transmitted and stored. The use of EEG compression is therefore becoming necessary. In this paper, we investigate the feasibility of using lossy compression to EEG data in EEG-based user authentication systems. Our experiments performed on a large scale of three EEG datasets indicate that using EEG lossy compression is feasible compared to using lossless one. Moreover, a threshold for information lost has been discovered and the system accuracy is unchanged if the threshold is lower than or equal 11%.

Original languageEnglish
Title of host publication2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages79-85
Number of pages7
Volume2017-May
ISBN (Electronic)9781509061815
ISBN (Print)9781509061839
DOIs
Publication statusPublished - 14 May 2017
Event2017 International Joint Conference on Neural Networks, IJCNN 2017 - Anchorage, United States
Duration: 14 May 201719 May 2017

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2017-May

Conference

Conference2017 International Joint Conference on Neural Networks, IJCNN 2017
CountryUnited States
CityAnchorage
Period14/05/1719/05/17

Fingerprint

Electroencephalography
Authentication
Biometrics
Experiments

Cite this

NGUYEN, T. B., Nguyen, D., Ma, W., & Tran, D. (2017). Investigating the possibility of applying EEG lossy compression to EEG-based user authentication. In 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings (Vol. 2017-May, pp. 79-85). [7965839] (Proceedings of the International Joint Conference on Neural Networks; Vol. 2017-May). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IJCNN.2017.7965839
NGUYEN, The Binh ; Nguyen, Dang ; Ma, Wanli ; Tran, Dat. / Investigating the possibility of applying EEG lossy compression to EEG-based user authentication. 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings. Vol. 2017-May IEEE, Institute of Electrical and Electronics Engineers, 2017. pp. 79-85 (Proceedings of the International Joint Conference on Neural Networks).
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NGUYEN, TB, Nguyen, D, Ma, W & Tran, D 2017, Investigating the possibility of applying EEG lossy compression to EEG-based user authentication. in 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings. vol. 2017-May, 7965839, Proceedings of the International Joint Conference on Neural Networks, vol. 2017-May, IEEE, Institute of Electrical and Electronics Engineers, pp. 79-85, 2017 International Joint Conference on Neural Networks, IJCNN 2017, Anchorage, United States, 14/05/17. https://doi.org/10.1109/IJCNN.2017.7965839

Investigating the possibility of applying EEG lossy compression to EEG-based user authentication. / NGUYEN, The Binh; Nguyen, Dang; Ma, Wanli; Tran, Dat.

2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings. Vol. 2017-May IEEE, Institute of Electrical and Electronics Engineers, 2017. p. 79-85 7965839 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2017-May).

Research output: A Conference proceeding or a Chapter in BookConference contribution

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NGUYEN TB, Nguyen D, Ma W, Tran D. Investigating the possibility of applying EEG lossy compression to EEG-based user authentication. In 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings. Vol. 2017-May. IEEE, Institute of Electrical and Electronics Engineers. 2017. p. 79-85. 7965839. (Proceedings of the International Joint Conference on Neural Networks). https://doi.org/10.1109/IJCNN.2017.7965839