SplashNet adds Rolling Time Normalization, Aggressive Channel Masking, and a Split‑and‑Share bilateral encoder to sEMG typing, slashing the emg2qwerty baseline’s zero‑shot and fine‑tuned CERs by 31 % and 21 % respectively with half the parameters.

We introduce MUelim, an algorithm for efficient motor unit decomposition that uses approximate joint diagonalization with a subtractive approach to rapidly identify motor units for advanced neuroprosthetic control.