Open AccessThe empirical mode decomposition-decision tree method to recognize the steady-state visual evoked potentials with wide frequency range
Author(s) -
Sahar Sadeghi
Publication year - 2018
Publication title -
journal of medical signals and sensors
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.337
H-Index - 21
ISSN - 2228-7477
DOI - 10.4103/jmss.jmss_20_18
Subject(s) - hilbert–huang transform , fast fourier transform , histogram , range (aeronautics) , pattern recognition (psychology) , artificial intelligence , computer science , speech recognition , evoked potential , signal (programming language) , matlab , mathematics , energy (signal processing) , algorithm , statistics , psychology , materials science , image (mathematics) , programming language , operating system , composite material , psychiatry
The empirical mode decomposition (EMD) is a technique to analyze the steady-state visual evoked potential (SSVEP) which decomposes the signal into its intrinsic mode functions (IMFs). Although for the limited stimulation frequency range, choosing the effective IMF leads to good results, but extending this range will seriously challenge the method so that even the combination of IMFs is associated with error.