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.

A scalable EEG foundation model leveraging 60,000+ hours of data, adaptable to any electrode setup, offering ready-to-use embeddings and state-of-the-art performance across diverse tasks.

NeurIPT, advancing scalable and generalizable EEG decoding across diverse BCI tasks.