  Biological measurements are often contaminated with large amounts of
non-stationary noise which require effective noise reduction techniques. We
present a new real-time deep learning algorithm which produces adaptively a
signal opposing the noise so that destructive interference occurs. As a proof
of concept, we demonstrate the algorithm's performance by reducing
electromyogram noise in electroencephalograms with the usage of a custom,
flexible, 3D-printed, compound electrode. With this setup, an average of 4dB
and a maximum of 10dB improvement of the signal-to-noise ratio of the EEG was
achieved by removing wide band muscle noise. This concept has the potential to
not only adaptively improve the signal-to-noise ratio of EEG but can be applied
to a wide range of biological, industrial and consumer applications such as
industrial sensing or noise cancelling headphones.
