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Related Experiment Video

Updated: Sep 16, 2025

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

Mitigating the Impact of Electrode Shift on Classification Performance in Electromyography Applications Using

Taichi Tanaka¹, Isao Nambu², Yasuhiro Wada²

¹Department of Science Technology of Innovation, Nagaoka University of Technology, Nagaoka 940-2188, Japan.

Sensors (Basel, Switzerland)

|July 12, 2025

Summary

This summary is machine-generated.

A new sliding-window normalization (SWN) technique improves electromyography (EMG) signal prediction accuracy by aligning amplitude across channels. This method mitigates performance loss from electrode shifts without requiring additional data or retraining.

Keywords:

DNN classification EMG electrode shift electromyography signal normalization z-score

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Last Updated: Sep 16, 2025

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Published on: April 12, 2016

Area of Science:

Biomedical Engineering
Signal Processing
Rehabilitation Technology

Background:

Electromyography (EMG) signals are crucial for prosthetics, assistive devices, and rehabilitation.
Performance limitations include cross-subject generalization issues, electrode shifts, and daily signal variability.
Existing solutions like transfer learning require additional data collection and retraining.

Purpose of the Study:

To investigate a novel sliding-window normalization (SWN) technique for real-time EMG prediction.
To address performance degradation caused by electrode shifts and daily variability.
To improve EMG-based application robustness without extensive retraining.

Main Methods:

Developed and validated a sliding-window normalization (SWN) technique.
SWN merges z-score normalization with sliding-window processing.
Experimental data from a right-arm trajectory-tracking task (three motion classes) was used for validation.

Main Results:

SWN mitigated accuracy degradation to -1.0% without retraining, a 6.6% improvement over the baseline.
The technique aligns EMG amplitude across channels, reducing sensitivity to electrode displacement.
Combining SWN with multi-position training surpassed the accuracy of the no-shift condition by 2.4%.

Conclusions:

SWN effectively mitigates EMG performance degradation due to electrode shifts and variability.
The method enables training with data from a single electrode position, simplifying practical application.
SWN offers a robust and efficient solution for real-time EMG-based human-computer interfaces.