IEEE Trans Biomed Eng. 2023 Jul;70(7):2058-2068. doi: 10.1109/TBME.2023.3234642. Epub 2023 Jun 19.
Surface electromyography (EMG) decomposition techniques have been developed to decode motor neuron activities non-invasively in the past decades, showing superior performance in human-machine interfaces such as gesture recognition and proportional control. However, neural decoding across multiple motor tasks and in real-time remains challenging, which limits its wide application. In this work, we proposed a real-time hand gesture recognition method by decoding motor unit (MU) discharges across multiple motor tasks ( 10) in a motion-wise way.
The EMG signals were first divided into numerous segments related to motions. The convolution kernel compensation algorithm was applied for each segment individually. The local MU filters, which indicate the MU-EMG correlation for each motion, were calculated iteratively in each segment and reused for global EMG decomposition to trace the MU discharges across motor tasks in real-time. The motion-wise decomposition method was applied on the high-density EMG signals recorded during twelve hand gesture tasks from eleven non-disabled participants. The neural feature of discharge count was extracted for gesture recognition based on five common classifiers.
On average, 164 ±34 MUs were identified for twelve motions from each subject, with a pulse-to-noise ratio of 32.1 ±5.6 dB. The average time cost of EMG decomposition in a sliding window of 50 ms was less than 5 ms. The average classification accuracy using a linear discriminant analysis classifier was 94.6 ±8.1%, which was significantly higher than that of a time-domain feature called root mean square. The superiority of the proposed method was also validated with a previously published EMG database comprising 65 gestures.
These results indicate the feasibility and superiority of the proposed method for MU identification and hand gesture recognition across multiple motor tasks, extending the potential applications of neural decoding in human-machine interfaces.
过去几十年来，表面肌电图 (EMG) 分解技术已经发展到可以非侵入性地解码运动神经元活动，在手势识别和比例控制等人机界面中表现出优异的性能。然而，跨多个运动任务的神经解码和实时性仍然具有挑战性，这限制了其广泛应用。在这项工作中，我们提出了一种实时手部运动识别方法，通过运动方式对多个运动任务 (10 个) 的运动单元 (MU) 放电进行解码。
首先将 EMG 信号分为与运动相关的多个片段。对每个片段单独应用卷积核补偿算法。在每个片段中迭代计算局部 MU 滤波器，该滤波器表示每个运动的 MU-EMG 相关性，并在全局 EMG 分解中重复使用，以实时跟踪跨运动任务的 MU 放电。在 11 名非残疾参与者的 12 个手部运动任务中记录的高密度 EMG 信号上应用运动方式分解方法。基于五个常用分类器，基于放电计数的神经特征提取用于手势识别。
平均而言，每个受试者的 12 个运动中可以识别出 164 ±34 个 MU，脉冲噪声比为 32.1 ±5.6 dB。50 ms 滑动窗口中 EMG 分解的平均时间成本小于 5 ms。使用线性判别分析分类器的平均分类准确率为 94.6 ±8.1%，明显高于称为均方根的时域特征。该方法的优越性还通过一个包含 65 个手势的先前发表的 EMG 数据库进行了验证。
这些结果表明，该方法在跨多个运动任务的 MU 识别和手部运动识别方面具有可行性和优越性，扩展了神经解码在人机界面中的潜在应用。
