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Online detection of auditory attention with mobile EEG: closing the loop with neurofeedback

Rob Zink, Stijn Proesmans, Alexander Bertrand, Sabine Van Huffel, Maarten De Vos
doi: https://doi.org/10.1101/218727
Rob Zink
1KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Kasteelpark Arenberg 10, 3001 Leuven, Belgium.
2imec, Leuven, Belgium.
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Stijn Proesmans
1KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Kasteelpark Arenberg 10, 3001 Leuven, Belgium.
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Alexander Bertrand
1KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Kasteelpark Arenberg 10, 3001 Leuven, Belgium.
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Sabine Van Huffel
1KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Kasteelpark Arenberg 10, 3001 Leuven, Belgium.
2imec, Leuven, Belgium.
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Maarten De Vos
3Engineering Department, Oxford University, Oxford, United Kingdom.
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Abstract

Auditory attention detection (AAD) is promising for use in auditory-assistive devices to detect to which sound the user is attending. Being able to train subjects in achieving high AAD performance would greatly increase its application potential. In order to do so an acceptable temporal resolution and online implementation are essential prerequisites. Consequently, users of an online AAD can be presented with feedback about their performance. Here we describe two studies that investigate the effects of online AAD with feedback. In the first study, we implemented a fully automated closed-loop system that allows for user-friendly recording environments. Subjects were presented online with visual feedback on their ongoing AAD performance. Following these results we implemented a longitudinal case study in which two subjects were presented with AAD sessions during four weeks. The results prove the feasibility of a fully working online (neuro)feedback system for AAD decoding. The detected changes in AAD for the feedback subject during and after training suggest that changes in AAD may be achieved via training. This is early evidence of such training effects and needs to be confirmed in future studies to evaluate training of AAD in more detail. Finally, the large number of sessions allowed to examine the correlation between the stimuli (i.e. acoustic stories) and AAD performance which was found to be significant. Future studies are suggested to evaluate their acoustic stimuli with care to prevent spurious associations.

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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 November 13, 2017.
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Online detection of auditory attention with mobile EEG: closing the loop with neurofeedback
Rob Zink, Stijn Proesmans, Alexander Bertrand, Sabine Van Huffel, Maarten De Vos
bioRxiv 218727; doi: https://doi.org/10.1101/218727
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Online detection of auditory attention with mobile EEG: closing the loop with neurofeedback
Rob Zink, Stijn Proesmans, Alexander Bertrand, Sabine Van Huffel, Maarten De Vos
bioRxiv 218727; doi: https://doi.org/10.1101/218727

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