Extended Data Fig. 7: Ablation Studies on different learning methods and different temporal signal data augmentations.
From: A substrate-less nanomesh receptor with meta-learning for rapid hand task recognition
a. Cosine similarity for supervised learning framework. b. Similarity based on TD-C learning. c, Examples of signal patterns before and after applying different data augmentations. d, Transfer accuracy comparison for learning models pretrained with different data augmentations predicting user numpad typing data. Jittering augmentation that does not change signal amplitude or frequencies allows the model to generate more transferable feature spaces. e, Summary table of prediction accuracy for different data augmentations. Compared to models trained with different data augmentations, the model trained with jittering shows 20% higher accuracy in average.
