# EMG Signal Classification Using Reflection Coefficients and Extreme Value Machine

arXiv: https://arxiv.org/abs/2106.10561 · HTML: https://mlsanity.com/paper/2106.10561/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 39 references that are in the map.
- First posted on arXiv 19 Jun 2021.

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

- [The Extreme Value Machine](https://mlsanity.com/paper/1506.06112/) (2015)
- [Presentation of a Recommender System with Ensemble Learning and Graph Embedding: A Case on MovieLens](https://mlsanity.com/paper/2008.01192/) (2020)
- [Convolutional Neural Networks for Speech Controlled Prosthetic Hands](https://mlsanity.com/paper/1910.01918/) (2019)
- [Automatic Open-World Reliability Assessment](https://mlsanity.com/paper/2011.05506/) (2020)

## Built on by (most cited first, 3 of 3)

- [EMGBench: Benchmarking Out-of-Distribution Generalization and Adaptation for Electromyography](https://mlsanity.com/paper/2410.23625/) (2024)
- [EMG-Based Feature Extraction and Classification for Prosthetic Hand Control](https://mlsanity.com/paper/2107.00733/) (2021)
- [Real-Time EMG Signal Classification via Recurrent Neural Networks](https://mlsanity.com/paper/2109.05674/) (2021)

## Abstract (arXiv)

Electromyography is a promising approach to the gesture recognition of humans if an efficient classifier with high accuracy is available. In this paper, we propose to utilize Extreme Value Machine (EVM) as a high-performance algorithm for the classification of EMG signals. We employ reflection coefficients obtained from an Autoregressive (AR) model to train a set of classifiers. Our experimental results indicate that EVM has better accuracy in comparison to the conventional classifiers approved in the literature based on K-Nearest Neighbors (KNN) and Support Vector Machine (SVM).