# Evaluating Classifier Confidence for Surface EMG Pattern Recognition

arXiv: https://arxiv.org/abs/2304.05898 · HTML: https://mlsanity.com/paper/2304.05898/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 12 references that are in the map.
- First posted on arXiv 12 Apr 2023.

## Builds on (most cited first, 4 of 4)

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [putEMG -- a surface electromyography hand gesture recognition dataset](https://mlsanity.com/paper/1907.07428/) (2019)
- [A Scale Mixture-Based Stochastic Model of Surface EMG Signals With Variable Variances](https://mlsanity.com/paper/1912.04580/) (2019)
- [EMG Pattern Recognition via Bayesian Inference with Scale Mixture-Based Stochastic Generative Models](https://mlsanity.com/paper/2107.09853/) (2021)

## Abstract (arXiv)

Surface electromyogram (EMG) can be employed as an interface signal for various devices and software via pattern recognition. In EMG-based pattern recognition, the classifier should not only be accurate, but also output an appropriate confidence (i.e., probability of correctness) for its prediction. If the confidence accurately reflects the likelihood of true correctness, then it will be useful in various application tasks, such as motion rejection and online adaptation. The aim of this paper is to identify the types of classifiers that provide higher accuracy and better confidence in EMG pattern recognition. We evaluate the performance of various discriminative and generative classifiers on four EMG datasets, both visually and quantitatively. The analysis results show that while a discriminative classifier based on a deep neural network exhibits high accuracy, it outputs a confidence that differs from true probabilities. By contrast, a scale mixture model-based classifier, which is a generative classifier that can account for uncertainty in EMG variance, exhibits superior performance in terms of both accuracy and confidence.