In practical scenarios involving the measurement of surface electromyography
(sEMG) in muscles, particularly those areas near the heart, one of the primary
sources of contamination is the presence of electrocardiogram (ECG) signals. To
assess the quality of real-world sEMG data more effectively, this study
proposes QASE-net, a new non-intrusive model that predicts the SNR of sEMG
signals. QASE-net combines CNN-BLSTM with attention mechanisms and follows an
end-to-end training strategy. Our experimental framework utilizes real-world
sEMG and ECG data from two open-access databases, the Non-Invasive Adaptive
Prosthetics Database and the MIT-BIH Normal Sinus Rhythm Database,
respectively. The experimental results demonstrate the superiority of QASE-net
over the previous assessment model, exhibiting significantly reduced prediction
errors and notably higher linear correlations with the ground truth. These
findings show the potential of QASE-net to substantially enhance the
reliability and precision of sEMG quality assessment in practical applications.

该研究提出了 QASE-net，这是一种新的非侵入式模型，用于预测 sEMG 信号的信噪比，实验结果显示 QASE-net 具有明显降低的预测误差和与真实结果更高的线性相关性，具备提高 sEMG 质量评估可靠性和精度的潜力。

一种用于表面肌电信号的非侵入性神经质量评估模型

A Non-Intrusive Neural Quality Assessment Model for Surface  Electromyography Signals

Transformers are state-of-the-art networks for most sequence processing
tasks. However, the self-attention mechanism often used in Transformers
requires large time windows for each computation step and thus makes them less
suitable for online signal processing compared to Recurrent Neural Networks
(RNNs). In this paper, instead of the self-attention mechanism, we use a
sliding window attention mechanism. We show that this mechanism is more
efficient for continuous signals with finite-range dependencies between input
and target, and that we can use it to process sequences element-by-element,
this making it compatible with online processing. We test our model on a finger
position regression dataset (NinaproDB8) with Surface Electromyographic (sEMG)
signals measured on the forearm skin to estimate muscle activities. Our
approach sets the new state-of-the-art in terms of accuracy on this dataset
while requiring only very short time windows of 3.5 ms at each inference step.
Moreover, we increase the sparsity of the network using Leaky-Integrate and
Fire (LIF) units, a bio-inspired neuron model that activates sparsely in time
solely when crossing a threshold. We thus reduce the number of synaptic
operations up to a factor of $\times5.3$ without loss of accuracy. Our results
hold great promises for accurate and fast online processing of sEMG signals for
smooth prosthetic hand control and is a step towards Transformers and Spiking
Neural Networks (SNNs) co-integration for energy efficient temporal signal
processing.

本文介绍了使用滑动窗口注意力机制代替自注意力机制，以便进行在线处理，同时在使用 Leaky-Integrate and Fire 单位时提高了网络的稀疏性，从而更准确快速地处理表皮肌电信号，为瞬态信号处理提供了一种快速准确的方法。