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Fast, Accurate, Unsupervised, and Time-Adaptive EEG-Based Auditory Attention Decoding for Neuro-steered Hearing Devices

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Brain-Computer Interface Research

Part of the book series: SpringerBriefs in Electrical and Computer Engineering ((BRIEFSELECTRIC))

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Abstract

More than 5% of the world’s population suffers from disabling hearing loss. Hearing aids and cochlear implants are crucial for improving their quality of life. However, current hearing technology does not work well in cocktail party scenarios, where several people talk simultaneously. This is mainly because the hearing device does not know which speaker the user is attending to, and so which speaker should be amplified relative to the background noise. In this project, we have developed novel signal processing algorithms for electroencephalography (EEG)-based auditory attention decoding to steer the hearing device towards the attended speaker based on the user’s attention. We propose algorithms that are fast, accurate, and able to adapt automatically to (changes in) the EEG data of individual users. These are crucial ingredients towards the realization of practically viable neuro-steered hearing devices.

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Notes

  1. 1.

    An extensive mathematical explanation can be found in, e.g., [8].

  2. 2.

    All details about the data and experiments can be found in [8].

  3. 3.

    All details about the data and experiments can be found in [11].

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Acknowledgements

This work was supported by an Aspirant Grant from the Research Foundation—Flanders (FWO) (for S. Geirnaert—1136219N), FWO project nos. G0A4918N and G081722N, the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement No 802895), and the Flemish Government (AI Research Program).

This project summary is written for the BCI Award 2022 and is based on the collection of papers in the PhD thesis of Simon Geirnaert [19].

Author information

Authors and Affiliations

  1. STADIUS Center for Dynamical Systems, Signal Processing, and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, Heverlee, Belgium

    Simon Geirnaert, Rob Zink & Alexander Bertrand

  2. ExpORL, Department of Neurosciences, KU Leuven, Leuven, Belgium

    Simon Geirnaert & Tom Francart

  3. Leuven.AI – KU Leuven Institute for AI, Leuven, Belgium

    Simon Geirnaert, Tom Francart & Alexander Bertrand

Authors
  1. Simon Geirnaert
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  2. Rob Zink
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  3. Tom Francart
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  4. Alexander Bertrand
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Corresponding author

Correspondence to Simon Geirnaert .

Editor information

Editors and Affiliations

  1. g.tec medical engineering GmbH, Schiedlberg, Oberösterreich, Austria

    Christoph Guger

  2. Cognitive Science Department, UC San Diego, La Jolla, CA, USA

    Brendan Allison

  3. RIKEN AIP, Tokyo Chuo-ku, Japan

    Tomasz M. Rutkowski

  4. The Institute of Neuroapproaches, Winter Springs, FL, USA

    Milena Korostenskaja

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Geirnaert, S., Zink, R., Francart, T., Bertrand, A. (2024). Fast, Accurate, Unsupervised, and Time-Adaptive EEG-Based Auditory Attention Decoding for Neuro-steered Hearing Devices. In: Guger, C., Allison, B., Rutkowski, T.M., Korostenskaja, M. (eds) Brain-Computer Interface Research. SpringerBriefs in Electrical and Computer Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-49457-4_4

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  • DOI: https://doi.org/10.1007/978-3-031-49457-4_4

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