  Current electromyography (EMG) pattern recognition (PR) models have been
shown to generalize poorly in unconstrained environments, setting back their
adoption in applications such as hand gesture control. This problem is often
due to limited training data, exacerbated by the use of supervised
classification frameworks that are known to be suboptimal in such settings. In
this work, we propose a shift to deep metric-based meta-learning in EMG PR to
supervise the creation of meaningful and interpretable representations. We use
a Siamese Deep Convolutional Neural Network (SDCNN) and contrastive triplet
loss to learn an EMG feature embedding space that captures the distribution of
the different classes. A nearest-centroid approach is subsequently employed for
inference, relying on how closely a test sample aligns with the established
data distributions. We derive a robust class proximity-based confidence
estimator that leads to a better rejection of incorrect decisions, i.e. false
positives, especially when operating beyond the training data domain. We show
our approach's efficacy by testing the trained SDCNN's predictions and
confidence estimations on unseen data, both in and out of the training domain.
The evaluation metrics include the accuracy-rejection curve and the
Kullback-Leibler divergence between the confidence distributions of accurate
and inaccurate predictions. Outperforming comparable models on both metrics,
our results demonstrate that the proposed meta-learning approach improves the
classifier's precision in active decisions (after rejection), thus leading to
better generalization and applicability.
