Deep learning (DL) has made tremendous contributions to image processing. Recently, the DL has also attracted attention in the specialized field of neural decoding from raw myoelectric signals (electromyograms, EMGs). However, to our knowledge, most existing methods require some measure of preprocessing of the raw EMGs. Moreover, research to date has not accounted for the variability in the signal during time sequences. In this paper, we propose a new convolutional neural network (CNN) structure that can directly process raw EMG signals for hand gesture classification. More specifically, we assess the effects of various window sizes and of two different EMG representations (time sequence and frequency spectra) on open EMG datasets. We found that the frequency spectra derived from raw EMGs is more suitable as the model input in the task of gesture classification. Meanwhile, the combination use of long window could improve the classification accuracy (CA) and the window of 1024 ms achieved the best results on two open datasets ([Formula: see text]% and [Formula: see text]%). Further, our model requires no feature extraction procedures and is comparable with the optimal combination of features and classifier used by the traditional methods in the performance of specific tasks.

中文翻译：

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使用具有不同尺度信号/光谱输入的卷积神经网络进行 EMG 模式识别

深度学习 (DL) 为图像处理做出了巨大贡献。最近，DL 在从原始肌电信号（肌电图，EMG）进行神经解码的专业领域也引起了关注。然而，据我们所知，大多数现有方法都需要对原始 EMG 进行某种程度的预处理。此外，迄今为止的研究还没有考虑到信号在时间序列中的可变性。在本文中，我们提出了一种新的卷积神经网络 (CNN) 结构，可以直接处理原始 EMG 信号以进行手势分类。更具体地说，我们评估了各种窗口大小和两种不同 EMG 表示（时间序列和频谱）对开放 EMG 数据集的影响。我们发现从原始 EMG 导出的频谱更适合作为手势分类任务中的模型输入。同时，长窗口的组合使用可以提高分类精度（CA），1024 ms的窗口在两个开放数据集（[公式：见文]%和[公式：见文]%）上取得了最好的效果。此外，我们的模型不需要特征提取程序，并且可以与传统方法在执行特定任务时使用的特征和分类器的最佳组合相媲美。