Real-time noise cancellation with Deep Learning

TL;DR

This paper introduces a real-time deep learning algorithm that adaptively cancels non-stationary noise in biological signals, demonstrated by improving EEG signal quality through noise reduction with a custom electrode.

Contribution

A novel real-time deep learning method for adaptive noise cancellation in biological signals, validated on EEG data with significant noise reduction.

Findings

Achieved an average of 4dB SNR improvement in EEG signals.

Maximum of 10dB SNR improvement in noise reduction.

Demonstrated potential for wide application in biological and industrial noise cancellation.

Abstract

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.