Real time identification of μ wave with wavelet neural networks

研究成果: Conference contribution

3 引文 斯高帕斯(Scopus)

摘要

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%.

原文English
主出版物標題Conference Proceedings - 1st International IEEE EMBS Conference on Neural Engineering
編輯Laura J. Wolf, Jodi L. Strock
發行者IEEE Computer Society
頁面218-220
頁數3
ISBN(電子)0780375793
DOIs
出版狀態Published - 2003 一月 1
事件1st International IEEE EMBS Conference on Neural Engineering - Capri Island, Italy
持續時間: 2003 三月 202003 三月 22

出版系列

名字International IEEE/EMBS Conference on Neural Engineering, NER
2003-January
ISSN(列印)1948-3546
ISSN(電子)1948-3554

Other

Other1st International IEEE EMBS Conference on Neural Engineering
國家Italy
城市Capri Island
期間03-03-2003-03-22

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Mechanical Engineering

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