TY - GEN
T1 - Improving neural decoding in the central auditory system using bio-inspired spectro-temporal representations and a generalized bilinear model
AU - Siahpoush, Shadi
AU - Erfani, Yousof
AU - Rode, Thilo
AU - Lim, Hubert H.
AU - Rouat, Jean
AU - Plourde, Eric
N1 - Publisher Copyright:
© 2015 IEEE.
Copyright:
Copyright 2016 Elsevier B.V., All rights reserved.
PY - 2015/11/4
Y1 - 2015/11/4
N2 - We study the impact of different encoding models and spectro-temporal representations on the accuracy of Bayesian decoding of neural activity recorded from the central auditory system. Two encoding models, a generalized linear model (GLM) and a generalized bilinear model (GBM), are compared along with three different spectro-temporal representations of the input stimuli: a spectrogram and two bio-inspired representations, i.e. a gammatone filter bank (GFB) and a spikegram. Signal to noise ratios between the reconstructed and original representations are used to evaluate the decoding, or reconstruction accuracy. We experimentally show that the reconstruction accuracy is best with the spikegram representation and worst with the spectrogram representation and, furthermore, that using a GBM instead of a GLM significantly increases the reconstruction accuracy. In fact, our results show that the spikegram reconstruction accuracy with a GBM fitting yields an SNR that is 3.3 dB better than when using the standard decoding approach of reconstructing a spectrogram with GLM fitting.
AB - We study the impact of different encoding models and spectro-temporal representations on the accuracy of Bayesian decoding of neural activity recorded from the central auditory system. Two encoding models, a generalized linear model (GLM) and a generalized bilinear model (GBM), are compared along with three different spectro-temporal representations of the input stimuli: a spectrogram and two bio-inspired representations, i.e. a gammatone filter bank (GFB) and a spikegram. Signal to noise ratios between the reconstructed and original representations are used to evaluate the decoding, or reconstruction accuracy. We experimentally show that the reconstruction accuracy is best with the spikegram representation and worst with the spectrogram representation and, furthermore, that using a GBM instead of a GLM significantly increases the reconstruction accuracy. In fact, our results show that the spikegram reconstruction accuracy with a GBM fitting yields an SNR that is 3.3 dB better than when using the standard decoding approach of reconstructing a spectrogram with GLM fitting.
UR - http://www.scopus.com/inward/record.url?scp=84953247410&partnerID=8YFLogxK
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U2 - 10.1109/EMBC.2015.7319550
DO - 10.1109/EMBC.2015.7319550
M3 - Conference contribution
C2 - 26737450
AN - SCOPUS:84953247410
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 5146
EP - 5150
BT - 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
Y2 - 25 August 2015 through 29 August 2015
ER -