# Detecting Domain Shifts in Myoelectric Activations: Challenges and Opportunities in Stream Learning

## TL;DR

This paper investigates the challenges of detecting domain shifts in non-stationary EMG signals using data stream learning, evaluating various drift detection methods on the Ninapro dataset to improve real-time EMG decoding stability.

## Contribution

It applies and assesses multiple drift detection techniques on EMG data, highlighting their limitations and proposing streaming approaches for better real-time domain shift detection.

## Key findings

- Current drift detection methods have limitations in EMG signal applications.
- Streaming-based approaches show promise for maintaining stable EMG models.
- Further research is needed to improve robustness and accuracy.

## Abstract

Detecting domain shifts in myoelectric activations poses a significant challenge due to the inherent non-stationarity of electromyography (EMG) signals. This paper explores the detection of domain shifts using data stream (DS) learning techniques, focusing on the DB6 dataset from the Ninapro database. We define domains as distinct time-series segments based on different subjects and recording sessions, applying Kernel Principal Component Analysis (KPCA) with a cosine kernel to pre-process and highlight these shifts. By evaluating multiple drift detection methods such as CUSUM, Page-Hinckley, and ADWIN, we reveal the limitations of current techniques in achieving high performance for real-time domain shift detection in EMG signals. Our results underscore the potential of streaming-based approaches for maintaining stable EMG decoding models, while highlighting areas for further research to enhance robustness and accuracy in real-world scenarios.