Sun Antong, Chen Xiang, Xu Mengjuan, Zhang Xu, Chen Xun
Department of Electronic Science and Technology, University of Science and Technology of China (USTC), Hefei, Anhui, China.
Front Neurosci. 2023 Jun 12;17:1174760. doi: 10.3389/fnins.2023.1174760. eCollection 2023.
In recent years, the effectiveness of a spiking neural network (SNN) for Electromyography (EMG) pattern recognition has been validated, but there is a lack of comprehensive consideration of the problems of heavy training burden, poor robustness, and high energy consumption in the application of actual myoelectric control systems. In order to explore the feasibility of the application of SNN in actual myoelectric control systems, this paper investigated an EMG pattern recognition scheme based on SNN. To alleviate the differences in EMG distribution caused by electrode shifts and individual differences, the adaptive threshold encoding was applied to gesture sample encoding. To improve the feature extraction ability of SNN, the leaky-integrate-and-fire (LIF) neuron that combines voltage-current effect was adopted as a spike neuron model. To balance recognition accuracy and power consumption, experiments were designed to determine encoding parameter and LIF neuron release threshold. By conducting the gesture recognition experiments considering different training test ratios, electrode shifts, and user independences on the nine-gesture high-density and low-density EMG datasets respectively, the advantages of the proposed SNN-based scheme have been verified. Compared with a Convolutional Neural Network (CNN), Long Short-Term Memory Network (LSTM) and Linear Discriminant Analysis (LDA), SNN can effectively reduce the number of repetitions in the training set, and its power consumption was reduced by 1-2 orders of magnitude. For the high-density and low-density EMG datasets, SNN improved the overall average accuracies by about (0.99 ~ 14.91%) under different training test ratios. For the high-density EMG dataset, the accuracy of SNN was improved by (0.94 ~ 13.76%) under electrode-shift condition and (3.81 ~ 18.95%) in user-independent case. The advantages of SNN in alleviating the user training burden, reducing power consumption, and improving robustness are of great significance for the implementation of user-friendly low-power myoelectric control systems.
近年来，脉冲神经网络（SNN）在肌电图（EMG）模式识别中的有效性已得到验证，但在实际肌电控制系统应用中，对训练负担重、鲁棒性差和能耗高的问题缺乏全面考虑。为了探索SNN在实际肌电控制系统中应用的可行性，本文研究了一种基于SNN的EMG模式识别方案。为了缓解因电极移位和个体差异导致的EMG分布差异，将自适应阈值编码应用于手势样本编码。为了提高SNN的特征提取能力，采用结合电压 - 电流效应的泄漏积分发放（LIF）神经元作为脉冲神经元模型。为了平衡识别精度和功耗，设计实验来确定编码参数和LIF神经元发放阈值。通过分别在九手势高密度和低密度EMG数据集上进行考虑不同训练测试比例、电极移位和用户独立性的手势识别实验，验证了所提出的基于SNN方案的优势。与卷积神经网络（CNN）、长短期记忆网络（LSTM）和线性判别分析（LDA）相比，SNN可以有效减少训练集中的重复次数，其功耗降低了1 - 2个数量级。对于高密度和低密度EMG数据集，在不同训练测试比例下，SNN将总体平均准确率提高了约（0.99 ~ 14.91%）。对于高密度EMG数据集，在电极移位条件下，SNN的准确率提高了（0.94 ~ 13.76%），在用户独立情况下提高了（3.81 ~ 18.95%）。SNN在减轻用户训练负担、降低功耗和提高鲁棒性方面的优势对于实现用户友好的低功耗肌电控制系统具有重要意义。
