Non-invasively Decoding of the Neural Drive in the Upper-Limb During Individual Finger Movements

Advancements in recording methods for neurophysiological signals, in particular, the use of high-density surface electromyography (HD-sEMG) results in increased spatial resolution but also opens the possibility to decode the neural drive to muscles. The higher number of electrode channels allows the detection of individual motoneuron action potentials and spike trains through the technique known as motor unit (MU) decomposition. Current studies on decomposition for decoding the neural drive to muscles focus on lower limb muscles, where the number of decoded MUs can be much higher compared to upper limb muscles [1]. This is mainly due to anatomical differences affecting signal propagation. Regardless, MU decomposition in the upper limb opens the exciting possibility to non-invasively decode the neural drive during manipulation tasks that involve a richer motor repertoire. Hence, in this study, neural drive decoded through motor unit decomposition from forearm muscles during finger movement is analysed. We decided to analyse the neural drive during individual finger movement as a starting point before moving to multi-finger gestures.

For this study, a publicly available dataset (the Hyser MVC dataset [2]) of HD-sEMG over forearm muscles during individual finger flexion and extension were analysed. The dataset comprises 256 EMG channels over the forearm flexor and extensor muscles (128 on each side of the forearm) during individual finger isometric contractions. Automatic motor unit decomposition was applied to this dataset using the method by Negro et al. [3]. The decomposition hyperparameters were chosen after empirical testing in sub-samples of the dataset to ensure reliable identification of MUs. We extracted the number of decomposed MU, their locations, and their waveforms’ amplitudes over the electrode grid.

On average, 6.22 ± 9.47 (n=20) MUs were decomposed from anterior forearm muscles during finger flexions and 3.46 ± 3.54 (n=20) from the posterior forearm during finger extensions, with 128 channels on each side. There was a very high inter-subject variability on the number of decoded MUs (see Fig. 1). Despite this, there were clear areas where the MU activations where clustered depending on which finger was flexing or extending (see Fig. 2 and Fig. 3). To verify this, the mean cosine similarities of the activation maps across participants and for each finger were computed, resulting in a grand average of 0.91 ± 0.03.

From these results, we can infer that, although the number of MUs decoded in forearm muscles is lower than what can be expected from lower limb muscles, more MUs could be decoded from specific subjects, which might be related to variable signal recording quality across sessions. Nonetheless, MU activity was clustered over specific finger-dependent areas, and this was consistent across subjects. Therefore, for isometric contractions, it is possible to consistently decode neural drives with similar spatial distributions for each individual finger movement with low inter-subject variability. This is crucial for non-invasive interfaces decoding neural drive that require increased selectivity. Additionally, knowledge of this activation areas could be used to apply moderate ablations by removing the less active electrodes depending on the movement studied, thus improving computation times.

References:

[1] A. S. Hassan et al., “Properties of Motor Units of Elbow and Ankle Muscles Decomposed Using High-Density Surface EMG,” 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany, 2019, pp. 3874-3878.

[2] Jiang, X., Dai, C., Liu, x., & Fan, J. (2021). Open Access Dataset and Toolbox of High-Density Surface Electromyogram Recordings (version 1.0.0). PhysioNet. https://doi.org/10.13026/ym7v-bh53.

[3] F. Negro, S. Muceli, A. M. Castronovo, A. Holobar and D. Farina, “Multi-channel intramuscular and surface EMG decomposition by convolutive blind source separation”, J Neural Eng, vol. 13, pp. 026027, 2016.

Recommended citation: R. Mio and A. A. Faisal. “Non-invasively Decoding of the Neural Drive in the Upper-Limb During Individual Finger Movements”, Neurophysiological Bases of Human Movement 2023.