Biomedical Engineering, James Watt School of Engineering, University of Glasgow, Glasgow, United Kingdom.
Bendable Electronics and Sensing Technologies (BEST) Group, James Watt School of Engineering, University of Glasgow, Glasgow, United Kingdom.
PLoS One. 2022 Nov 21;17(11):e0277974. doi: 10.1371/journal.pone.0277974. eCollection 2022.
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.
生物测量往往受到大量非平稳噪声的污染，需要有效的降噪技术。我们提出了一种新的实时深度学习算法，该算法自适应地产生一个与噪声相反的信号，从而产生相消干扰。作为概念验证，我们通过使用定制的、灵活的、3D 打印的复合电极来减少脑电图中的肌电图噪声，展示了该算法的性能。通过这种设置，通过去除宽带肌肉噪声，EEG 的信噪比平均提高了 4dB，最高提高了 10dB。这个概念不仅有潜力自适应地提高 EEG 的信噪比，还可以应用于广泛的生物、工业和消费应用，如工业传感或降噪耳机。
