Rossato Julien, Hug François, Tucker Kylie, Gibbs Ciara, Lacourpaille Lilian, Farina Dario, Avrillon Simon
Nantes Université, Laboratory "Movement, Interactions, Performance" (UR 4334), Nantes, France.
Université Côte d'Azur, LAMHESS, Nice, France.
Elife. 2024 Oct 2;12:RP88670. doi: 10.7554/eLife.88670.
Decoding the activity of individual neural cells during natural behaviours allows neuroscientists to study how the nervous system generates and controls movements. Contrary to other neural cells, the activity of spinal motor neurons can be determined non-invasively (or minimally invasively) from the decomposition of electromyographic (EMG) signals into motor unit firing activities. For some interfacing and neuro-feedback investigations, EMG decomposition needs to be performed in real time. Here, we introduce an open-source software that performs real-time decoding of motor neurons using a blind-source separation approach for multichannel EMG signal processing. Separation vectors (motor unit filters) are optimised for each motor unit from baseline contractions and then re-applied in real time during test contractions. In this way, the firing activity of multiple motor neurons can be provided through different forms of visual feedback. We provide a complete framework with guidelines and examples of recordings to guide researchers who aim to study movement control at the motor neuron level. We first validated the software with synthetic EMG signals generated during a range of isometric contraction patterns. We then tested the software on data collected using either surface or intramuscular electrode arrays from five lower limb muscles (gastrocnemius lateralis and medialis, vastus lateralis and medialis, and tibialis anterior). We assessed how the muscle or variation of contraction intensity between the baseline contraction and the test contraction impacted the accuracy of the real-time decomposition. This open-source software provides a set of tools for neuroscientists to design experimental paradigms where participants can receive real-time feedback on the output of the spinal cord circuits.
解码自然行为期间单个神经细胞的活动，使神经科学家能够研究神经系统如何产生和控制运动。与其他神经细胞不同，脊髓运动神经元的活动可以通过将肌电图（EMG）信号分解为运动单位放电活动来进行非侵入性（或微创性）测定。对于一些接口和神经反馈研究，EMG分解需要实时进行。在这里，我们介绍一种开源软件，该软件使用盲源分离方法对多通道EMG信号进行处理，从而实现运动神经元的实时解码。从基线收缩中为每个运动单位优化分离向量（运动单位滤波器），然后在测试收缩期间实时重新应用。通过这种方式，可以通过不同形式的视觉反馈提供多个运动神经元的放电活动。我们提供了一个完整的框架，包括指南和记录示例，以指导旨在在运动神经元水平研究运动控制的研究人员。我们首先用一系列等长收缩模式期间生成的合成EMG信号验证了该软件。然后，我们使用来自五个下肢肌肉（外侧和内侧腓肠肌、外侧和内侧股四头肌以及胫骨前肌）的表面或肌内电极阵列收集的数据对该软件进行了测试。我们评估了肌肉或基线收缩与测试收缩之间收缩强度的变化如何影响实时分解的准确性。这种开源软件为神经科学家提供了一套工具，用于设计实验范式，使参与者能够获得关于脊髓回路输出的实时反馈。
