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A Comparison of Regularization Methods in Forward and Backward Models for Auditory Attention Decoding

Wong, DDE; Fuglsang, SA; Hjortkjær, J; Ceolini, E; Slaney, M; De Cheveigne, A; (2018) A Comparison of Regularization Methods in Forward and Backward Models for Auditory Attention Decoding. Frontiers in Neuroscience , 12 , Article 531. 10.3389/fnins.2018.00531. Green open access

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Abstract

The decoding of selective auditory attention from noninvasive electroencephalogram (EEG) data is of interest in brain computer interface and auditory perception research. The current state-of-the-art approaches for decoding the attentional selection of listeners are based on linear mappings between features of sound streams and EEG responses (forward model), or vice versa (backward model). It has been shown that when the envelope of attended speech and EEG responses are used to derive such mapping functions, the model estimates can be used to discriminate between attended and unattended talkers. However, the predictive/reconstructive performance of the models is dependent on how the model parameters are estimated. There exist a number of model estimation methods that have been published, along with a variety of datasets. It is currently unclear if any of these methods perform better than others, as they have not yet been compared side by side on a single standardized dataset in a controlled fashion. Here, we present a comparative study of the ability of different estimation methods to classify attended speakers from multi-channel EEG data. The performance of the model estimation methods is evaluated using different performance metrics on a set of labeled EEG data from 18 subjects listening to mixtures of two speech streams. We find that when forward models predict the EEG from the attended audio, regularized models do not improve regression or classification accuracies. When backward models decode the attended speech from the EEG, regularization provides higher regression and classification accuracies.

Type: Article
Title: A Comparison of Regularization Methods in Forward and Backward Models for Auditory Attention Decoding
Open access status: An open access version is available from UCL Discovery
DOI: 10.3389/fnins.2018.00531
Publisher version: https://doi.org/10.3389/fnins.2018.00531
Language: English
Additional information: © 2018 Wong, Fuglsang, Hjortkjær, Ceolini, Slaney and de Cheveigné. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (http://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > The Ear Institute
URI: https://discovery.ucl.ac.uk/id/eprint/10067285
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