Targeted Muscle Reinnervation for Prosthetic Control

• TMR is a neurosurgical technique that reroutes nerves from amputated limbs to residual proximal muscles, enabling biological amplification of motor signals.
• Advanced methodologies leverage high-density EMG arrays and blind-source separation to extract and decode multiple degrees of freedom from reinnervated muscles.
• Research shows that TMR preserves central neural coordination and motor synergies, facilitating robust, low-latency prosthetic control with high reliability.

Targeted Muscle Reinnervation (TMR) is a neurosurgical technique in which peripheral nerves that originally innervated muscles of an amputated limb are surgically rerouted (“reinnervated”) into residual or spare muscles proximal to the amputation site. After successful reinnervation, these target muscles serve as biological amplifiers for efferent motor commands, enabling high-information-content signals to be recovered via electromyographic (EMG) recording and exploited for advanced prosthetic device control. Recent advances leverage high-density intramuscular arrays and blind-source separation techniques to extract motor neuron spike trains, decode multiple degrees of freedom (DoFs), and reconstruct the dimensionality and modularity of neural drive even after profound biomechanical reconfiguration.

1. Surgical Paradigms and Biological Reorganization

Classic TMR involves coapting major motor nerves (e.g., median, ulnar, radial) that originally terminated in distal limb musculature to the motor branches of denervated proximal muscles such as the biceps, brachialis, triceps, latissimus dorsi, or pectoralis minor. In the case of polyfascicular donor nerves, the entire trunk may be attached en bloc or, in more refined Regenerative Peripheral Nerve Interface (RPNI) procedures, partitioned fascicle-wise into separate muscle grafts. Over 6–12 months post-operatively, regenerating axons hyper-reinnervate the muscle volumes, creating a complex amalgam of motor units originally subserving diverse hand and wrist functions. This produces so-called "polyfunctional" muscles, which serve as new readout points for the CNS’s intended but no longer biomechanically coupled outputs (Ferrante et al., 2024).

2. Concepts in Signal Acquisition and Decomposition

Biopotential signals from reinnervated muscles are obtained using high-density intramuscular arrays: 40 platinum electrodes, ∼140 μm diameter, 2 cm coverage, 500 μm spacing, 20 μm double-sided polyimide backbone (Ferrante et al., 2024, Ferrante et al., 23 Jan 2026). Sampling is at ≥10 kHz, with simultaneous 16-bit digitization across all channels. Channels undergo artifact rejection (RMS noise threshold >15 μV) and high-pass digital filtering (cutoff 1,000 Hz) prior to decomposition.

EMG signals are modeled as convolutive mixtures of sparse spike-train sources (the output of individual motor neurons) convolved with their action-potential waveforms, with additional noise. Formally, letting $x_i(k)$ denote the $k$th sample of channel $i$, and $h_{ij}(\ell)$ the MUAP kernel for source $j$ on channel $i$:

$$x_i(k) = \sum_{j=1}^n \sum_{\ell=0}^{L-1} h_{ij}(\ell) s_j(k-\ell) + n_i(k)$$

Multichannel, convolutional blind-source separation (CKC, ICA-style objectives with orthogonality and regularization constraints) is used to estimate the demixing filters that recover the individual motor-unit spike trains $s_j(k)$ (Ferrante et al., 2024, Andalib et al., 2019). Detected spike trains are validated by pulse-to-noise ratio, spike-count, and the silhouette index.

3. Central Coordination, Motor Unit Synergies, and Dimensionality

Recent work demonstrates that after TMR, reinnervated muscles preserve substantial aspects of central neural coordination even though the mechanistic (joint-level) agonist–antagonist couplings are abolished. When neural signals for agonist and antagonist actions are rerouted to independent muscles, a substantial fraction of the same motor units ($\approx$40%) are nonetheless co-recruited for both tasks (Ferrante et al., 23 Jan 2026). This is established by tracking spike trains and matching MUAP templates across task conditions by correlation coefficients (ρ ≥ 0.85) and spatial fingerprint overlap.

Firing-rate descriptors (median firing rate, coefficient of variation) and interspike interval statistics are computed and compared using non-parametric statistics (Wilcoxon signed-rank, α = 0.05). The shared recruitment of units is accompanied by low-dimensional common synaptic input, as demonstrated by non-negative matrix factorization (NNMF) applied to the matrix of firing rates. NNMF reveals latent factors that organize the motor-unit pools into task- or synergy-specific groups. In TMR, the dimensionality of these neural manifolds generally matches or exceeds the number of intended joint movements/phantom tasks, showing that the CNS’s modular drive is preserved and recapitulated in the new target (Ferrante et al., 2024, Ferrante et al., 23 Jan 2026).

4. Disentangling Polyfunctional Neural Drives

In conventional TMR, signals recorded from EMG are typically mixtures of neural commands for multiple limb functions, creating challenges for decoding. The use of high-density arrays and advanced source separation (BSS/CKC/ICA) enables the recovery of multiple, separable motor-unit clusters within a single reinnervated muscle. Clustering based on low-frequency pairwise cross-correlations of spike trains, together with silhouette analysis, demonstrates that 43–54% of units are task-specific (i.e., unique to a particular intended movement), while the remainder are shared or polyfunctional (Ferrante et al., 2024). NNMF further delineates up to 5–6 independent neural drives ("components") within a single recording site, corresponding to individual degrees of freedom or functional movement classes.

A summary of separability and yield is presented below:

Metric	Value	Notes
Task-specific unit fraction	43–54% per muscle	Averaged across 23 tasks
Number of separable factors	l=t (typically 5–6)	Each matches a distinct phantom movement/task
Motor unit recurrence	70% in ≥70% of repetitions	Indicates high reliability for control

Task-specific units demonstrate high selectivity for imagined or attempted limb actions, and the presence of both specific and shared units suggests robust encoding of multi-DoF motor commands and preservation of CNS “motor synergy” structure post-TMR (Ferrante et al., 2024).

5. Signal Processing for Multi-DoF Prosthetic Control

Motor neuron spike train signals are extracted and then projected into lower-dimensional spaces using unsupervised techniques (PCA, NNMF, VARIMAX). For $X \in \mathbb{R}^{M \times T}$, the binned spike-count matrix, PCA extracts dominant eigenvectors defining neural axes. An orthogonal rotation $R$ (solved via Procrustes alignment or unsupervised VARIMAX) maps these to canonical DoF axes, achieving near-orthogonality and sparse loadings corresponding to joint or finger movements (Andalib et al., 2019).

The resultant projected signals $Z = R Y$ are mapped linearly to prosthetic kinematics:

$\theta_i(t) = g_i z_i(t)$

where gains $g_i$ are calibrated by regression. Accuracy across up to three DoFs is measured by $R^2$ (variance explained), routinely exceeding $0.7$ in both training and test data (Andalib et al., 2019). Space-time multiplexing strategies allow reduction in active channels to as few as 32 per block (100 ms), with negligible decrement in $R^2$. These operations are low-latency, linear, and practicable on embedded systems, supporting real-time, simultaneous, proportional multi-DoF control without the need for labor-intensive supervised model training.

6. Functional and Clinical Implications

The preservation of central agonist–antagonist coupling, coexistence of shared and task-specific motor units, and the resulting low-dimensional structure of neural drive constitute a fundamental rationale for extending TMR-based interfaces to more physiological prosthesis control (Ferrante et al., 23 Jan 2026, Ferrante et al., 2024). Decoding strategies that explicitly exploit the residual common inputs and modularity—by identifying and leveraging shared motor units or latent NNMF/PCA factors—offer improvement over classic surface-EMG approaches, which treat each site as an independent channel.

Multi-channel and multi-component decoding enables single-muscle interfaces to supply 5–6 independent control signals, greatly exceeding traditional TMR EMG channel counts. Persisting central coupling could be harnessed for modulating prosthetic joint impedance (variable-stiffness actuation) and for driving artificial proprioceptive/sensory feedback channels encoding torque or position. Robustness to electrode reduction and the generalizability of unsupervised metric learning minimize patient burden and enable wearable, practical systems (Andalib et al., 2019).

7. Broader Impact on Neuromotor Interface Design

TMR, when coupled to high-density signal acquisition and modern source separation, enables not only improved prosthetic command bandwidth but also provides a quantitative window into CNS reorganization after amputation. The measured neural manifolds, the mix of shared/task-specific units, and the preservation of low-dimensional common synaptic input demonstrate that TMR reroutes anatomical and functional neural architecture without erasing the CNS’s intrinsic modularity. These findings motivate next-generation myoelectric interfaces focused on decoding spike trains at the motor unit or latent-factor level, with anticipated advances in the intuitiveness, dimensionality, and responsiveness of prosthetic limb control (Ferrante et al., 23 Jan 2026, Ferrante et al., 2024, Andalib et al., 2019).