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A Comparison of Temporal Response Function Estimation Methods for Auditory Attention Decoding

View ORCID ProfileDaniel D.E. Wong, View ORCID ProfileSøren A. Fuglsang, View ORCID ProfileJens Hjortkjær, Enea Ceolini, View ORCID ProfileMalcolm Slaney, View ORCID ProfileAlain de Cheveigné
doi: https://doi.org/10.1101/281345
Daniel D.E. Wong
1Laboratoire des Systèmes Perceptifs, UMR 8248, CNRS, Paris, France
2Département d’Études Cognitives, École Normale Supérieure, PSL Research University, Paris, France
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  • For correspondence: ddewong@gmail.com
Søren A. Fuglsang
3Department of Electrical Engineering, Danmarks Tekniske Universitet, Lyngby, Denmark
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Jens Hjortkjær
3Department of Electrical Engineering, Danmarks Tekniske Universitet, Lyngby, Denmark
4Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
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Enea Ceolini
5Institute of Neuroinformatics, University of Zürich, Zürich, Switzerland
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Malcolm Slaney
6AI Machine Perception, Google, Mountain View, CA, USA
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Alain de Cheveigné
1Laboratoire des Systèmes Perceptifs, UMR 8248, CNRS, Paris, France
2Département d’Études Cognitives, École Normale Supérieure, PSL Research University, Paris, France
7Ear Institute, University College London, London, United Kingdom
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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 temporal response functions (TRFs). In the current context, a TRF is a function that facilitates a mapping between features of sound streams and EEG responses. It has been shown that when the envelope of attended speech and EEG responses are used to derive TRF mapping functions, the TRF model predictions can be used to discriminate between attended and unattended talkers. However, the predictive performance of the TRF models is dependent on how the TRF model parameters are estimated. There exist a number of TRF 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 TRF estimation methods to classify attended speakers from multi-channel EEG data. The performance of the TRF 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.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted March 13, 2018.
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A Comparison of Temporal Response Function Estimation Methods for Auditory Attention Decoding
Daniel D.E. Wong, Søren A. Fuglsang, Jens Hjortkjær, Enea Ceolini, Malcolm Slaney, Alain de Cheveigné
bioRxiv 281345; doi: https://doi.org/10.1101/281345
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A Comparison of Temporal Response Function Estimation Methods for Auditory Attention Decoding
Daniel D.E. Wong, Søren A. Fuglsang, Jens Hjortkjær, Enea Ceolini, Malcolm Slaney, Alain de Cheveigné
bioRxiv 281345; doi: https://doi.org/10.1101/281345

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