L2HiEMG synthesizer: A CycleGAN-driven computational tool for synthesizing enhanced muscle dynamics from lower-level intramuscular electromyography signals

Intramuscular electromyography (iEMG) recorded at low contraction levels often exhibits overlapping signal characteristics, particularly within the range of mild to moderate muscle activations. This overlap reduces the discriminability of contraction states in the 25%–50% maximum voluntary contraction (MVC) range, making it challenging for iEMG-based prediction systems to achieve reliable classification. As a consequence, such systems frequently produce intermittent or discontinuous outputs, which compromise the naturalness and stability of prosthetic and exoskeleton control. To address this limitation, we propose the L2HiEMG synthesizer, a neural network model built upon the CycleGAN framework. The model is designed to transform low-intensity iEMG signals (25% MVC) into their higher-intensity counterparts (50% MVC), thereby simulating the nonlinear dynamics of motor unit recruitment and muscle fiber activation. Leveraging the dual Generator–Discriminator structure of CycleGAN and its cycle-consistency constraint, the proposed method effectively addresses issues arising from unpaired data, sample imbalance, and ambiguous label correspondence. Experimental evaluations demonstrated that the L2HiEMG synthesizer can generate high-fidelity signals at higher intensity. In the time domain, the synthesized signals exhibited mean and standard deviation differences of less than 0.01 and 0.04, respectively, with correlation coefficients consistently exceeding 0.99. Frequency-domain analyses further validated the accuracy of the generated signals, showing minimal deviations in zero-crossing rate ( < 0.08), signal energy ( < 0.01), and power spectral density ( < 0.03). Taken together, these results confirmed the model’s capability to produce physiologically realistic high-intensity iEMG signals, thereby offering a promising strategy to enhance the stability and responsiveness of iEMG-driven assistive control systems.