Real time identification of μ wave with wavelet neural networks

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

In the rehabilitation of paralyzed patients, the functional electrical stimulation (FES) or prostheses is often adopted in clinical practice. One of the key issues in these new technologies is the source for generating control commands. The brain computer interface (BCI) creates an alternative pathway from the brain potentials. In this investigation, we construct real-time system to percept the voluntary movement of right thumb as a basic study of BCI. We combine the wavelet transformation and neural network as Wavelet Neural Network (WNN) identify the attempt of voluntary thumb movement. Three types of classification methods: realtime classification without network update, real-time classification with update and conevntional power spectral analyses are compared, and it was found that the WNN with off-line retraining shows better successful rate up to 80%.

Original languageEnglish
Title of host publicationConference Proceedings - 1st International IEEE EMBS Conference on Neural Engineering
EditorsLaura J. Wolf, Jodi L. Strock
PublisherIEEE Computer Society
Pages218-220
Number of pages3
ISBN (Electronic)0780375793
DOIs
Publication statusPublished - 2003 Jan 1
Event1st International IEEE EMBS Conference on Neural Engineering - Capri Island, Italy
Duration: 2003 Mar 202003 Mar 22

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
Volume2003-January
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554

Other

Other1st International IEEE EMBS Conference on Neural Engineering
CountryItaly
CityCapri Island
Period03-03-2003-03-22

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Mechanical Engineering

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