Electromyography (EMG) is a source of neural information for controlling neuroprosthetic devices. To enhance the information content of conventional bipolar EMG, high-density EMG systems include tens to hundreds of closely spaced electrodes that non-invasively record the electrical activity of muscles with high spatial resolution. Despite the advantages of relying on multiple signal sources, however, variations in electrode-skin contact impedance and noise remain challenging for multichannel myocontrol systems. These spatial and temporal non-stationarities negatively impact the control accuracy and therefore substantially limit the clinical viability of high-density EMG techniques. Here, we propose an adaptive algorithm for automatic artefact/noise detection and attenuation for high-density EMG control. The method infers the presence of noise in each EMG channel by spectro-temporal measures of signal similarity. These measures are then used for establishing a scoring system based on an adaptive weighting and reinforcement formulation. The method was experimentally tested as a pre-processing step for a multi-class discrimination problem of 4-digit activation. The approach was proven to enhance the discriminative information content of high-density EMG signals, as well as to attenuate non-stationary artefacts, with improvements in accuracy and robustness of the classification.

中文翻译：

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高密度肌电信号的自适应空间滤波可减少噪声和伪影对肌电控制的影响

肌电图（EMG）是用于控制神经修复装置的神经信息的来源。为了增强常规双极EMG的信息内容，高密度EMG系统包括数十至数百个紧密间隔的电极，这些电极以高空间分辨率无创地记录肌肉的电活动。尽管依赖于多个信号源具有许多优点，但是，对于多通道肌控系统，电极-皮肤接触阻抗和噪声的变化仍然具有挑战性。这些空间和时间上的非平稳性会对控制精度产生负面影响，因此会大大限制高密度肌电图技术的临床可行性。在这里，我们提出了一种自适应算法，用于高密度肌电图控制的自动伪影/噪声检测和衰减。该方法通过信号相似度的频谱时间测量来推断每个EMG通道中是否存在噪声。这些措施然后用于基于自适应加权和增强公式建立评分系统。该方法已作为4位数激活的多类别识别问题的预处理步骤进行了实验测试。实践证明，该方法可增强高密度EMG信号的判别信息内容，并能减弱​​非平稳伪影，并提高分类的准确性和鲁棒性。该方法已作为4位数激活的多类别区分问题的预处理步骤进行了实验测试。实践证明，该方法可增强高密度EMG信号的判别信息内容，并能减弱​​非平稳伪像，并提高分类的准确性和鲁棒性。该方法已作为4位数激活的多类别区分问题的预处理步骤进行了实验测试。实践证明，该方法可增强高密度EMG信号的判别信息内容，并能减弱​​非平稳伪影，并提高分类的准确性和鲁棒性。