There is a growing interest in decomposing high-density surface electromyography (HDsEMG) into motor unit spike trains to improve knowledge on the neural control of muscle contraction.However, the reliability of decomposition approaches is sometimes questioned, especially because they require manual editing of the outputs. We aimed to assess the inter-operator reliability of the identification of motor unit spike trains. Eight operators with varying experience in HDsEMG decomposition were provided with the same data extracted using the convolutive kernel compensation method. They were asked to manually edit them following established procedures. Data included signals from three lower leg muscles and different submaximal intensities. After manual analysis, 126 ± 5 motor units were retained (range across operators: 119-134). A total of 3380 rate of agreement values were calculated (28 pairwise comparisons ´ 11 contractions/muscles ´ 4-28 motor units). The median rate of agreement value was 99.6%. Inter-operator reliability was excellent for both mean discharge rate and time at recruitment (intraclass correlation coefficient > 0.99). These results show that when provided with the same decomposed data and the same basic instructions, operators converge toward almost identical results. Our data have been made available so that they can be used for training new operators.

This study aimed to determine whether neural drive is redistributed between muscles during a fatiguing isometric contraction, and if so, whether the initial level of common synaptic input between these muscles constrains this redistribution. We studied two muscle groups: triceps surae (14 participants) and quadriceps (15 participants). Participants performed a series of submaximal isometric contractions and a torque-matched contraction maintained until task failure. We used high-density surface electromyography to identify the behavior of 1874 motor units from the soleus, gastrocnemius medialis (GM), gastrocnemius lateralis(GL), rectus femoris, vastus lateralis (VL), and vastus medialis(VM). We assessed the level of common drive between muscles in absence of fatigue using a coherence analysis. We also assessed the redistribution of neural drive between muscles during the fatiguing contraction through the correlation between their cumulative spike trains (index of neural drive). The level of common drive between VL and VM was significantly higher than that observed for the other muscle pairs, including GL-GM. The level of common drive increased during the fatiguing contraction, but the differences between muscle pairs persisted. We also observed a strong positive correlation of neural drive between VL and VM during the fatiguing contraction (r=0.82). This was not observed for the other muscle pairs, including GL-GM, which exhibited differential changes in neural drive. These results suggest that less common synaptic input between muscles allows for more flexible coordination strategies during a fatiguing task, i.e., differential changes in neural drive across muscles. The role of this flexibility on performance remains to be elucidated.

There is a growing interest in decomposing high-density surface electromyography (HDsEMG) into motor unit spike trains to improve knowledge on the neural control of muscle contraction. However, the reliability of decomposition approaches is sometimes questioned, especially because they require manual editing of the outputs. We aimed to assess the inter-operator reliability of the identification of motor unit spike trains. Eight operators with varying experience in HDsEMG decomposition were provided with the same data extracted using the convolutive kernel compensation method. They were asked to manually edit them following established procedures. Data included signals from three lower leg muscles and different contraction intensities. After manual analysis, 126 ± 5 motor units were retained (range across operators: 119-134). A total of 3380 rate of agreement values were calculated (28 pairwise comparisons x 11 contractions/muscles x 4-28 motor units). The median rate of agreement value was 99.6%. Inter-operator reliability was excellent for both mean discharge rate and time at recruitment (intraclass correlation coefficient > 0.99). These results show that when provided with the same decomposed data and the same basic instructions, operators converge toward almost identical results. Our data have been made available so that they can be used for training new operators.

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 discharge 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 spinal motor neurons using a blind-source separation approach for multichannel EMG signal processing. Separation vectors (motor unit filters) are identified for each motor unit from a baseline contraction and then re-applied in real-time during test contractions. In this way, the discharge activity of multiple motor units can be provided as visual feedback in real-time. 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 tested the software on data collected using either grids of surface electrodes 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 interface 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.

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 discharge 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 spinal motor neurons using a blind-source separation approach for multichannel EMG signal processing. Separation vectors (motor unit filters) are identified for each motor unit from a baseline contraction and then re-applied in real-time during test contractions. In this way, the discharge activity of multiple motor units can be provided as visual feedback in real-time. 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 tested the software on data collected using either grids of surface electrodes 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 interface 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.