Abstract

A new approach for recognizing single-channel EEG pattern induced by acupuncture on acupoint of Zusanli was presented. EEG was decomposed by ensemble empirical mode decomposition (EEMD) into a small number of intrinsic mode functions (IMFs) based on intrinsic local characteristic time scale. The power, standard deviation, and analytic amplitude of first two IMFs expressed the difference between normal EEG and EEG affected by acupuncture. The two EEG patterns could be classified by extreme learning machine (ELM) with faster and better classification performance compared with BP. Similar classification accuracy of different channels indicated the global EEG pattern affected by acupuncture.

Published in

Advances in cognitive neurodynamics
2016, number: 5, pages: 485-491
Title: Advances in Cognitive Neurodynamics (V)
ISBN: 978-981-10-0207-6
Publisher: Springer

SLU Authors

• Liljenström, Hans

  • Department of Energy and Technology, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Signal Processing


Publication identifier

DOI: https://doi.org/10.1007/978-981-10-0207-6_66

Permanent link to this page (URI)

https://res.slu.se/id/publ/73834