This poster presents my Laidlaw research project on the computational efficiency of surface electromyography (sEMG) motor-unit decomposition. I compared classical blind source separation and autoencoder-based approaches to investigate how algorithmic budget affects motor-unit recovery and processing time, with the aim of informing more energy-efficient wearable neural interfaces.