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

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.