Adaptive EMG Pattern Classification via Probabilistic Knowledge Transfer With Scale Mixture-Based Bayesian Sequential Learning

Electromyogram (EMG) signals, measured non-invasively from the skin surface, reflect human motion intentions and enable device control through pattern classification, particularly in applications such as myoelectric prostheses. However, continuous use of EMG-based interfaces remains challenging due...

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Bibliographic Details

Published in:	IEEE Transactions on Neural Systems and Rehabilitation Engineering
Main Authors:	Seitaro Yoneda, Akira Furui
Format:	Article
Language:	English
Published:	IEEE 2025-01-01
Subjects:	Electromyogram (EMG) Bayesian updating sequential learning adaptive pattern classification
Online Access:	https://ieeexplore.ieee.org/document/11079723/