TY - JOUR
T1 - Biometric identification of listener identity from frequency following responses to speech
AU - Llanos, Fernando
AU - Xie, Zilong
AU - Chandrasekaran, Bharath
N1 - Publisher Copyright:
© 2019 IOP Publishing Ltd.
PY - 2019/7/23
Y1 - 2019/7/23
N2 - Objective. We investigate the biometric specificity of the frequency following response (FFR), an EEG marker of early auditory processing that reflects phase-locked activity from neural ensembles in the auditory cortex and subcortex (Chandrasekaran and Kraus 2010, Bidelman, 2015a, 2018, Coffey et al 2017b). Our objective is two-fold: demonstrate that the FFR contains information beyond stimulus properties and broad group-level markers, and to assess the practical viability of the FFR as a biometric across different sounds, auditory experiences, and recording days. Approach. We trained the hidden Markov model (HMM) to decode listener identity from FFR spectro-temporal patterns across multiple frequency bands. Our dataset included FFRs from twenty native speakers of English or Mandarin Chinese (10 per group) listening to Mandarin Chinese tones across three EEG sessions separated by days. We decoded subject identity within the same auditory context (same tone and session) and across different stimuli and recording sessions. Main results. The HMM decoded listeners for averaging sizes as small as one single FFR. However, model performance improved for larger averaging sizes (e.g. 25 FFRs), similarity in auditory context (same tone and day), and lack of familiarity with the sounds (i.e. native English relative to native Chinese listeners). Our results also revealed important biometric contributions from frequency bands in the cortical and subcortical EEG. Significance. Our study provides the first deep and systematic biometric characterization of the FFR and provides the basis for biometric identification systems incorporating this neural signal.
AB - Objective. We investigate the biometric specificity of the frequency following response (FFR), an EEG marker of early auditory processing that reflects phase-locked activity from neural ensembles in the auditory cortex and subcortex (Chandrasekaran and Kraus 2010, Bidelman, 2015a, 2018, Coffey et al 2017b). Our objective is two-fold: demonstrate that the FFR contains information beyond stimulus properties and broad group-level markers, and to assess the practical viability of the FFR as a biometric across different sounds, auditory experiences, and recording days. Approach. We trained the hidden Markov model (HMM) to decode listener identity from FFR spectro-temporal patterns across multiple frequency bands. Our dataset included FFRs from twenty native speakers of English or Mandarin Chinese (10 per group) listening to Mandarin Chinese tones across three EEG sessions separated by days. We decoded subject identity within the same auditory context (same tone and session) and across different stimuli and recording sessions. Main results. The HMM decoded listeners for averaging sizes as small as one single FFR. However, model performance improved for larger averaging sizes (e.g. 25 FFRs), similarity in auditory context (same tone and day), and lack of familiarity with the sounds (i.e. native English relative to native Chinese listeners). Our results also revealed important biometric contributions from frequency bands in the cortical and subcortical EEG. Significance. Our study provides the first deep and systematic biometric characterization of the FFR and provides the basis for biometric identification systems incorporating this neural signal.
KW - biometric identification system
KW - electroencephalogram
KW - frequency following response
KW - hidden Markov model
KW - machine learning
KW - neural plasticity
UR - http://www.scopus.com/inward/record.url?scp=85070184995&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85070184995&partnerID=8YFLogxK
U2 - 10.1088/1741-2552/ab1e01
DO - 10.1088/1741-2552/ab1e01
M3 - Article
C2 - 31039552
AN - SCOPUS:85070184995
SN - 1741-2560
VL - 16
JO - Journal of Neural Engineering
JF - Journal of Neural Engineering
IS - 5
M1 - 056004
ER -