EEG-based detection of the locus of auditory attention with convolutional neural networks

Abstract

In a multi-speaker scenario, the human auditory system is able to attend to one particular speaker of interest and ignore the others. It has been demonstrated that it is possible to use electroencephalography (EEG) signals to infer to which speaker someone is attending by relating the neural activity to the speech signals. However, classifying auditory attention within a short time interval remains the main challenge. We present a convolutional neural network-based approach to extract the locus of auditory attention (left/right) without knowledge of the speech envelopes. Our results show that it is possible to decode the locus of attention within 1 to 2 s, with a median accuracy of around 81%. These results are promising for neuro-steered noise suppression in hearing aids, in particular in scenarios where per-speaker envelopes are unavailable.

Data availability

Code used for training and evaluating the network has been made available at https://github.com/exporl/locus-of-auditory-attention-cnn. The CNN models used to generate the results shown in the paper are also available at that location. The dataset used in this study had been made available earlier at https://zenodo.org/record/3377911.

The following previously published data sets were used

Article and author information

Author details

  1. Servaas Vandecappelle

    Department of Neurosciences, Katholieke Universiteit Leuven, Leuven, Belgium
    For correspondence
    servaas.vandecappelle@gmail.com
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0266-7293
  2. Lucas Deckers

    Department of Neurosciences, Katholieke Universiteit Leuven, Leuven, Belgium
    Competing interests
    The authors declare that no competing interests exist.
  3. Neetha Das

    Department of Neurosciences, Katholieke Universiteit Leuven, Leuven, Belgium
    Competing interests
    The authors declare that no competing interests exist.
  4. Amir Hossein Ansari

    Department of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium
    Competing interests
    The authors declare that no competing interests exist.
  5. Alexander Bertrand

    Department of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4827-8568
  6. Tom Francart

    Dept. of Neurosciences, Katholieke Universiteit Leuven, Leuven, Belgium
    For correspondence
    tom.francart@kuleuven.be
    Competing interests
    The authors declare that no competing interests exist.

Funding

KU Leuven Special Research Fund (C14/16/057)

  • Tom Francart

KU Leuven Special Research Fund (C24/18/099)

  • Alexander Bertrand

Research Foundation Flanders (1.5.123.16N)

  • Alexander Bertrand

Research Foundation Flanders (G0A4918N)

  • Alexander Bertrand

European Research Council (637424)

  • Tom Francart

European Research Council (802895)

  • Alexander Bertrand

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: The experiment was approved by the Ethics Committee Research UZ/KU Leuven (S57102) and every participant signed an informed consent form approved by the same commitee.

Reviewing Editor

  1. Barbara G Shinn-Cunningham, Carnegie Mellon University, United States

Publication history

  1. Received: February 28, 2020
  2. Accepted: April 28, 2021
  3. Accepted Manuscript published: April 30, 2021 (version 1)
  4. Version of Record published: May 24, 2021 (version 2)

Copyright

© 2021, Vandecappelle et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Servaas Vandecappelle
  2. Lucas Deckers
  3. Neetha Das
  4. Amir Hossein Ansari
  5. Alexander Bertrand
  6. Tom Francart
(2021)
EEG-based detection of the locus of auditory attention with convolutional neural networks
eLife 10:e56481.
https://doi.org/10.7554/eLife.56481

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