Our study complements previous research and provides information suggesting that efficient low-density EEG online decoding is within reach.

Target speaker identification is essential for speech enhancement algorithms in assistive devices aimed toward helping the hearing impaired. Several recent studies have reported that target speaker identification is possible through electroencephalography (EEG) recordings. If the EEG system could be reduced to acceptable size while retaining the signal quality, hearing aids could benefit from the integration with concealed EEG. To compare the performance of a multichannel around-the-ear EEG system with high-density cap EEG recordings an envelope tracking algorithm was applied in a competitive speaker paradigm. The data from 20 normal hearing listeners were concurrently collected from the traditional state-of-the-art laboratory wired EEG system and a wireless mobile EEG system with two bilaterally-placed around-the-ear electrode arrays (cEEGrids). The results show that the cEEGrid ear-EEG technology captured neural signals that allowed the identification of the attended speaker above chance-level, with 69.3% accuracy, while cap-EEG signals resulted in the accuracy of 84.8%. Further analyses investigated the influence of ear-EEG signal quality and revealed that the envelope tracking procedure was unaffected by variability in channel impedances. We conclude that the quality of concealed ear-EEG recordings as acquired with the cEEGrid array has potential to be used in the brain-computer interface steering of hearing aids.

These findings are further evidence for the feasibility of around the-ear EEG recordings and demonstrate that well described ERPs can be measured. We conclude that concealed behind-the-ear EEG recordings can be an alternative to classical cap EEG acquisition for auditory attention monitoring.

Objective: It has been suggested that the next major advancement in hearing aid (HA) technology needs to include cognitive feedback from the user to control HA functionality. In order to enable automatic brainwave-steered HA adjustments, attentional processes underlying speech-in-noise perception in aided hearing-impaired individuals need to be better understood. Here, we addressed the influence of two important factors for the listening performance of HA users -hearing aid processing and motivation -by analysing ongoing neural responses during long-term listening to continuous noisy speech.Methods: Sixteen normal-hearing (NH) and 15 linearly aided hearing-impaired (aHI) participants listened to an audiobook recording embedded in realistic speech babble noise at individually adjusted signal-to-noise ratios (SNRs). A HA simulator was used for simulating a directional microphone setting as well as for providing individual amplification. To assess listening performance behaviourally, participants answered questions about the contents of the audiobook. We manipulated (1) the participants' motivation by offering a monetary reward for good listening performance in one half of the measurements and (2) the SNR by engaging/disengaging the directional microphone setting. During the speech-in-noise task, electroencephalography (EEG) signals were recorded using wireless, mobile hardware. EEG correlates of listening performance were investigated using EEG impulse responses, as estimated using the cross-correlation between the recorded EEG signal and the temporal envelope of the audiobook at the output of the HA simulator.Results: At the behavioural level, we observed better performance for the NH listeners than for the aHI listeners. Furthermore, the directional microphone setting led to better performance for both participant groups, and when the directional microphone setting was disengaged motivation also improved the performance of the aHI participants. Analysis of the EEG impulse responses showed faster N1-P2 responses for both groups and larger N2 peak amplitudes for the aHI group when the directional microphone setting was activated, but no physiological correlates of motivation. Significance:The results of this study indicate that motivation plays an important role for speech understanding in noise. In terms of neuro-steered HAs, our results suggest that the latency of attentional processes is influenced by HA-induced stimulus changes, which can potentially be used for inferring benefit from noise suppression processing automatically. Further research is necessary to identify the neural correlates of motivation as an exclusive top-down process and to combine such features with HA-driven ones for online HA adjustments.