<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>This paper presents a novel method to enhance the performance of Independent Component Analysis (ICA) of myoelectric signal by decomposing the signal into components originating from different muscles. First, we use Multi run ICA (MICA) algorithm to separate the muscle activities. Pattern classification of the separated signal is performed in the second step with a back propagation neural network. The focus of this work is to establish a simple, yet robust system that can be used to identify subtle complex hand actions and gestures for control of prosthesis and other computer assisted devices. Testing was conducted using several single shot experiments conducted with five subjects. The results indicate that the system is able to classify four different wrist actions with near 100% accuracy.</p></abstract>

This paper presents a novel method to enhance the performance of Independent Component Analysis (ICA) of myoelectric signal by decomposing the signal into components originating from different muscles. First, we use Multi run ICA (MICA) algorithm to separate the muscle activities. Pattern classification of the separated signal is performed in the second step with a back propagation neural network. The focus of this work is to establish a simple, yet robust system that can be used to identify subtle complex hand actions and gestures for control of prosthesis and other computer assisted devices. Testing was conducted using several single shot experiments conducted with five subjects. The results indicate that the system is able to classify four different wrist actions with near 100% accuracy.

Hybrid Feature Selection for Myoelectric Signal Classification Using MICA

School of Electrical and Computing Engineering, RMIT University Melbourne, Australia

Journal of Electrical Engineering

{"article-title":"Hybrid Feature Selection for Myoelectric Signal Classification Using MICA"}

This paper presents a novel method to enhance the performance of Independent Component Analysis (ICA) of myoelectric signal by decomposing the signal into...

Hybrid Feature Selection for Myoelectric Signal Classification Using MICA

Data publikacji: 07 cze 2011

Zakres stron: 93 - 99

DOI: https://doi.org/10.2478/v10187-010-0013-8

Słowa kluczowe
blind source separation (BSS), independent component analysis (ICA), surface electromyogram (sEMG), myoelectric signal (MES), source separation, human computer interface (HCI)

This content is open access.

Hybrid Feature Selection for Myoelectric Signal Classification Using MICA

Data publikacji: 07 cze 2011

Zakres stron: 93 - 99

DOI: https://doi.org/10.2478/v10187-010-0013-8

Słowa kluczoweblind source separation (BSS), independent component analysis (ICA), surface electromyogram (sEMG), myoelectric signal (MES), source separation, human computer interface (HCI)

This content is open access.

Słowa kluczowe
blind source separation (BSS), independent component analysis (ICA), surface electromyogram (sEMG), myoelectric signal (MES), source separation, human computer interface (HCI)