Recently, ultrasound imaging of muscle contractions has been used by several research groups to infer volitional motor intent of the user, and has shown promise as a novel muscle computer interface. Learning spatiotemporal features from ultrasound image sequences is challenging because of deformations introduced by probe repositioning. The image features are sensitive to probe placement and even small displacements during donning and doffing of the probe compromises the classification accuracy while using a model trained on a previous session. This requires frequent recalibration. Deep learning based feature extractors have been shown to be invariant to translation, rotation and slight deformations. In this study, we investigate the feasibility of wavelet-based deep scattering features to preserve motion classification accuracy across multiple donning and doffing sessions.