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.

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.

Metrics

  • 3,849
    views
  • 532
    downloads
  • 76
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  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

Share this article

https://doi.org/10.7554/eLife.56481

Further reading

    1. Neuroscience
    Sergio Casas-Tinto, Nuria Garcia-Guillen, María Losada-Perez
    Short Report

    As the global population ages, the prevalence of neurodegenerative disorders is fast increasing. This neurodegeneration as well as other central nervous system (CNS) injuries cause permanent disabilities. Thus, generation of new neurons is the rosetta stone in contemporary neuroscience. Glial cells support CNS homeostasis through evolutionary conserved mechanisms. Upon damage, glial cells activate an immune and inflammatory response to clear the injury site from debris and proliferate to restore cell number. This glial regenerative response (GRR) is mediated by the neuropil-associated glia (NG) in Drosophila, equivalent to vertebrate astrocytes, oligodendrocytes (OL), and oligodendrocyte progenitor cells (OPCs). Here, we examine the contribution of NG lineages and the GRR in response to injury. The results indicate that NG exchanges identities between ensheathing glia (EG) and astrocyte-like glia (ALG). Additionally, we found that NG cells undergo transdifferentiation to yield neurons. Moreover, this transdifferentiation increases in injury conditions. Thus, these data demonstrate that glial cells are able to generate new neurons through direct transdifferentiation. The present work makes a fundamental contribution to the CNS regeneration field and describes a new physiological mechanism to generate new neurons.

    1. Neuroscience
    Mihály Vöröslakos, Yunchang Zhang ... György Buzsáki
    Tools and Resources

    Brain states fluctuate between exploratory and consummatory phases of behavior. These state changes affect both internal computation and the organism’s responses to sensory inputs. Understanding neuronal mechanisms supporting exploratory and consummatory states and their switching requires experimental control of behavioral shifts and collecting sufficient amounts of brain data. To achieve this goal, we developed the ThermoMaze, which exploits the animal’s natural warmth-seeking homeostatic behavior. By decreasing the floor temperature and selectively heating unmarked areas, we observed that mice avoided the aversive state by exploring the maze and finding the warm spot. In its design, the ThermoMaze is analogous to the widely used water maze but without the inconvenience of a wet environment and, therefore, allows the collection of physiological data in many trials. We combined the ThermoMaze with electrophysiology recording, and report that spiking activity of hippocampal CA1 neurons during sharp-wave ripple events encode the position of mice. Thus, place-specific firing is not confined to locomotion and associated theta oscillations but persist during waking immobility and sleep at the same location. The ThermoMaze will allow for detailed studies of brain correlates of immobility, preparatory–consummatory transitions, and open new options for studying behavior-mediated temperature homeostasis.