Evaluation of Joint Auditory Attention Decoding and Adaptive Binaural Beamforming Approach for Hearing Devices with Attention Switching

Beamforming is a common technique used to improve speech intelligibility and listening comfort of hearing aids users in a noisy environment. Traditional hearing aids beamforming algorithms require the a priori knowledge of the auditory of the listener, which may not be available in real applications. Recent advances in electroencephalography (EEG) offer a potential non-invasive solution to this problem. The listener’s auditory is derived from the EEG signals through auditory decoding algorithms and can be used as an input to the beamforming algorithms. In [1], a joint auditory decoding and adaptive beamforming algorithm framework by correlating the envelope of beamforming output and the EEG signal was proposed to improve the beamformer’s robustness against decoding error. Consistent performance improvement was demonstrated on an EEG database recorded on listeners with fixed . In this study, we present the evaluation results of this joint formulation on a new EEG dataset collected on subjects with dynamic switch. We demonstrate not only the joint framework’s performance improvement against decoding errors, but also its ability to capture listener’s dynamic switch.