Private authentication keys based on wearable device EEG recordings

H. Yang; V. Mihajlovic; T. Ignatenko

doi:10.23919/EUSIPCO.2017.8081349

Private authentication keys based on wearable device EEG recordings

H. Yang, V. Mihajlovic, T. Ignatenko

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

6 Citations (Scopus)

3 Downloads (Pure)

Abstract

In this paper, we study an Electroencephalography (EEG) based biometric authentication system with privacy protection. We use motor imagery EEG, recorded using a wearable wireless device, as our biometric modality. To obtain EEG-based authentication keys we employ the fuzzy-commitment like scheme with soft-information at the decoder, see Ignatenko and Willems [2014]. In this work we study the effect of multi-level quantization together with binary encoding of EEG biometric at the encoder on the system performance, when EEG feature vectors have limited length. We demonstrate our findings on an experimental EEG dataset of ten healthy subjects.

Original language	English
Title of host publication	25th European Signal Processing Conference (EUSIPCO 2017), 28 August - 2 September 2017, Kos, Greece
Pages	956-960
Number of pages	5
ISBN (Electronic)	9780992862671
DOIs	https://doi.org/10.23919/EUSIPCO.2017.8081349
Publication status	Published - 23 Oct 2017
Event	25th European Signal Processing Conference, EUSIPCO 2017 - Kos, Greece Duration: 28 Aug 2017 → 2 Sept 2017 Conference number: 25 https://www.eusipco2017.org/

Conference

Conference	25th European Signal Processing Conference, EUSIPCO 2017
Abbreviated title	EUSIPCO 2017
Country/Territory	Greece
City	Kos
Period	28/08/17 → 2/09/17
Internet address	https://www.eusipco2017.org/

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10.23919/EUSIPCO.2017.8081349

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title = "Private authentication keys based on wearable device EEG recordings",

abstract = "In this paper, we study an Electroencephalography (EEG) based biometric authentication system with privacy protection. We use motor imagery EEG, recorded using a wearable wireless device, as our biometric modality. To obtain EEG-based authentication keys we employ the fuzzy-commitment like scheme with soft-information at the decoder, see Ignatenko and Willems [2014]. In this work we study the effect of multi-level quantization together with binary encoding of EEG biometric at the encoder on the system performance, when EEG feature vectors have limited length. We demonstrate our findings on an experimental EEG dataset of ten healthy subjects.",

author = "H. Yang and V. Mihajlovic and T. Ignatenko",

year = "2017",

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doi = "10.23919/EUSIPCO.2017.8081349",

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booktitle = "25th European Signal Processing Conference (EUSIPCO 2017), 28 August - 2 September 2017, Kos, Greece",

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T1 - Private authentication keys based on wearable device EEG recordings

AU - Yang, H.

AU - Mihajlovic, V.

AU - Ignatenko, T.

N1 - Conference code: 25

PY - 2017/10/23

Y1 - 2017/10/23

N2 - In this paper, we study an Electroencephalography (EEG) based biometric authentication system with privacy protection. We use motor imagery EEG, recorded using a wearable wireless device, as our biometric modality. To obtain EEG-based authentication keys we employ the fuzzy-commitment like scheme with soft-information at the decoder, see Ignatenko and Willems [2014]. In this work we study the effect of multi-level quantization together with binary encoding of EEG biometric at the encoder on the system performance, when EEG feature vectors have limited length. We demonstrate our findings on an experimental EEG dataset of ten healthy subjects.

AB - In this paper, we study an Electroencephalography (EEG) based biometric authentication system with privacy protection. We use motor imagery EEG, recorded using a wearable wireless device, as our biometric modality. To obtain EEG-based authentication keys we employ the fuzzy-commitment like scheme with soft-information at the decoder, see Ignatenko and Willems [2014]. In this work we study the effect of multi-level quantization together with binary encoding of EEG biometric at the encoder on the system performance, when EEG feature vectors have limited length. We demonstrate our findings on an experimental EEG dataset of ten healthy subjects.

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U2 - 10.23919/EUSIPCO.2017.8081349

DO - 10.23919/EUSIPCO.2017.8081349

M3 - Conference contribution

SN - 978-0-9928626-7-1

SP - 956

EP - 960

BT - 25th European Signal Processing Conference (EUSIPCO 2017), 28 August - 2 September 2017, Kos, Greece

T2 - 25th European Signal Processing Conference, EUSIPCO 2017

Y2 - 28 August 2017 through 2 September 2017

ER -

Private authentication keys based on wearable device EEG recordings

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