Objective. Smart hearing aids which can decode the focus of a user's attention could considerably improve comprehension levels in noisy environments. Methods for decoding auditory attention from electroencephalography (EEG) have attracted considerable interest for this reason. Recent studies suggest that the integration of deep neural networks (DNNs) into existing auditory attention decoding algorithms is highly beneficial, although it remains unclear whether these enhanced algorithms can perform robustly in different real-world scenarios. To this end, we sought to characterise the performance of DNNs at reconstructing the envelope of an attended speech stream from EEG recordings in different listening conditions. In addition, given the relatively sparse availability of EEG data, we investigate possibility of applying subject-independent algorithms to EEG recorded from unseen individuals. Approach. Both linear models and nonlinear DNNs were employed to decode the envelope of clean speech from EEG recordings, with and without subject-specific information. The mean behaviour, as well as the variability of the reconstruction, was characterised for each model. We then trained subject-specific linear models and DNNs to reconstruct the envelope of speech in clean and noisy conditions, and investigated how well they performed in different listening scenarios. We also established that these models can be used to decode auditory attention in competing-speaker scenarios. Main results. The DNNs offered a considerable advantage over their linear counterpart at reconstructing the envelope of clean speech. This advantage persisted even when subject-specific information was unavailable at the time of training. The same DNN architectures generalised to a distinct dataset, which contained EEG recorded under a variety of listening conditions. In competing-speakers and speech-in-noise conditions, the DNNs significantly outperformed the linear models. Finally, the DNNs offered a considerable improvement over the linear approach at decoding auditory attention in competing-speakers scenarios. Significance. We present the first detailed study into the extent to which DNNs can be employed for reconstructing the envelope of an attended speech stream. We conclusively demonstrate that DNNs have the ability to improve the reconstruction of the attended speech envelope. The variance of the reconstruction error is shown to be similar for both DNNs and the linear model. Overall, DNNs are demonstrated to show promise for real-world auditory attention decoding, since they perform well in multiple listening conditions and generalise to data recorded from unseen participants.

The ear-EEG has emerged as a promising candidate for real-world wearable brain monitoring. While experimental studies have validated several applications of ear-EEG, the source-sensor relationship for neural sources from across the brain surface has not yet been established. In addition, modeling of the ear-EEG sensitivity to sources of artifacts is still missing. Through volume conductor modeling, the sensitivity of various configurations of ear-EEG is established for a range of neural sources, in addition to ocular artifact sources for the blink, vertical saccade, and horizontal saccade eye movements. Results conclusively support the introduction of ear-EEG into conventional EEG paradigms for monitoring neural activity that originates from within the temporal lobes, while also revealing the extent to which ear-EEG can be used for sources further away from these regions. The use of ear-EEG in scenarios prone to ocular artifacts is also supported, through the demonstration of proportional scaling of artifacts and neural signals in various configurations of ear-EEG. The results from this study can be used to support both existing and prospective experimental ear-EEG studies and applications in the context of sensitivity to both neural sources and ocular artifacts.