  Speech enhancement is widely used as a front-end to improve the speech
quality in many audio systems, while it is hard to extract the target speech in
multi-talker conditions without prior information on the speaker identity. It
was shown that the auditory attention on the target speaker can be decoded from
the electroencephalogram (EEG) of the listener implicitly. In this work, we
therefore propose a novel end-to-end brain-assisted speech enhancement network
(BASEN), which incorporates the listeners' EEG signals and adopts a temporal
convolutional network together with a convolutional multi-layer cross attention
module to fuse EEG-audio features. Considering that an EEG cap with sparse
channels exhibits multiple benefits and in practice many electrodes might
contribute marginally, we further propose two channel selection methods, called
residual Gumbel selection and convolutional regularization selection. They are
dedicated to tackling training instability and duplicated channel selections,
respectively. Experimental results on a public dataset show the superiority of
the proposed BASEN over existing approaches. The proposed channel selection
methods can significantly reduce the amount of informative EEG channels with a
negligible impact on the performance.
