Chang Jason, Phinyomark Angkoon, Scheme Erik
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:3339-3342. doi: 10.1109/EMBC44109.2020.9175260.
In recent years, many electromyography (EMG) benchmark databases have been made publicly available to the myoelectric control research community. Many small laboratories that lack the instrumentation, access, and experience needed to collect quality EMG data have used these benchmark datasets to explore and propose new signal processing and pattern recognition algorithms. It is widely accepted that noise contamination can affect the performance of myoelectric control systems, and so useful datasets should maintain good signal quality to ensure accurate results for proposed EMG-based gesture recognition systems. Despite the availability and adoption of benchmarks datasets, however, the quality of the EMG signals in these benchmarks has not yet been examined. In this study, the signal quality of the Non-Invasive Adaptive Prosthetics (NinaPro) dataset, the most widely known publicly available benchmark database to date, was comprehensively investigated with the goals of: 1) reporting the level of noise contamination in each NinaPro sub-dataset, 2) proposing signal quality criteria for assessing EMG datasets, 3) analyzing the effect of signal quality on classification performance, and 4) examining the quality of the data labels.
近年来，许多肌电图（EMG）基准数据库已向肌电控制研究社区公开。许多缺乏收集高质量EMG数据所需仪器、访问权限和经验的小型实验室，已使用这些基准数据集来探索和提出新的信号处理和模式识别算法。人们普遍认为，噪声污染会影响肌电控制系统的性能，因此有用的数据集应保持良好的信号质量，以确保基于EMG的手势识别系统能得出准确结果。然而，尽管有基准数据集可供使用和采用，但这些基准中的EMG信号质量尚未得到检验。在本研究中，对迄今为止最广为人知的公开可用基准数据库——非侵入性自适应假肢（NinaPro）数据集的信号质量进行了全面调查，目标如下：1）报告每个NinaPro子数据集中的噪声污染水平；2）提出评估EMG数据集的信号质量标准；3）分析信号质量对分类性能的影响；4）检验数据标签的质量。
