The DELTA dataset, namely ”Dense Electromyography for Long-Term Adaptive control”, holds significance in the realm of prosthetic applications, featuring High Density surface Electromyography data collected over an extended duration. It serves as a valuable resource for training data-driven models and testing them under conditions that closely emulate real-world prosthetic applications. Recognizing the temporal variability in EMG data, constructing models that are agnostic to such fluctuations could significantly enhance the efficacy of machine learning models in prosthetic applications.