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The Empirical Mode Decomposition-Decision Tree Method to Recognize the Steady-State Visual Evoked Potentials with Wide Frequency Range

BACKGROUND: 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,...

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Autores principales: Sadeghi, Sahar, Maleki, Ali
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Medknow Publications & Media Pvt Ltd 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6293644/
https://www.ncbi.nlm.nih.gov/pubmed/30603614
http://dx.doi.org/10.4103/jmss.JMSS_20_18
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author Sadeghi, Sahar
Maleki, Ali
author_facet Sadeghi, Sahar
Maleki, Ali
author_sort Sadeghi, Sahar
collection PubMed
description BACKGROUND: 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. METHODS: Stimulation frequencies ranged from 6 to 16 Hz with an interval of 0.5 Hz were generated using Psychophysics toolbox of MATLAB. SSVEP signal was recorded from six subjects. The EMD was used to extract the effective IMFs. Two features, including the frequency related to the peak of spectrum and normalized local energy in this frequency, were extracted for each of six conditions (each IMF, the combination of two consecutive IMFs and the combination of all three IMFs). RESULTS: The instantaneous frequency histogram and the recognition accuracy diagram indicate that for wide stimulation frequency range, not only one IMF, but also the combination of IMFs does not have desirable efficiency. Total recognition accuracy of the proposed method was 79.75%, while the highest results obtained from the EMD-fast Fourier transform (FFT) and the CCA were 72.05% and 77.31%, respectively. CONCLUSION: The proposed method has improved the recognition rate more than 2.4% and 7.7% compared to the CCA and EMD-FFT, respectively, by providing the solution for situations with wide stimulation frequency range.
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spelling pubmed-62936442019-01-02 The Empirical Mode Decomposition-Decision Tree Method to Recognize the Steady-State Visual Evoked Potentials with Wide Frequency Range Sadeghi, Sahar Maleki, Ali J Med Signals Sens Original Article BACKGROUND: 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. METHODS: Stimulation frequencies ranged from 6 to 16 Hz with an interval of 0.5 Hz were generated using Psychophysics toolbox of MATLAB. SSVEP signal was recorded from six subjects. The EMD was used to extract the effective IMFs. Two features, including the frequency related to the peak of spectrum and normalized local energy in this frequency, were extracted for each of six conditions (each IMF, the combination of two consecutive IMFs and the combination of all three IMFs). RESULTS: The instantaneous frequency histogram and the recognition accuracy diagram indicate that for wide stimulation frequency range, not only one IMF, but also the combination of IMFs does not have desirable efficiency. Total recognition accuracy of the proposed method was 79.75%, while the highest results obtained from the EMD-fast Fourier transform (FFT) and the CCA were 72.05% and 77.31%, respectively. CONCLUSION: The proposed method has improved the recognition rate more than 2.4% and 7.7% compared to the CCA and EMD-FFT, respectively, by providing the solution for situations with wide stimulation frequency range. Medknow Publications & Media Pvt Ltd 2018 /pmc/articles/PMC6293644/ /pubmed/30603614 http://dx.doi.org/10.4103/jmss.JMSS_20_18 Text en Copyright: © 2018 Journal of Medical Signals & Sensors http://creativecommons.org/licenses/by-nc-sa/4.0 This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.
spellingShingle Original Article
Sadeghi, Sahar
Maleki, Ali
The Empirical Mode Decomposition-Decision Tree Method to Recognize the Steady-State Visual Evoked Potentials with Wide Frequency Range
title The Empirical Mode Decomposition-Decision Tree Method to Recognize the Steady-State Visual Evoked Potentials with Wide Frequency Range
title_full The Empirical Mode Decomposition-Decision Tree Method to Recognize the Steady-State Visual Evoked Potentials with Wide Frequency Range
title_fullStr The Empirical Mode Decomposition-Decision Tree Method to Recognize the Steady-State Visual Evoked Potentials with Wide Frequency Range
title_full_unstemmed The Empirical Mode Decomposition-Decision Tree Method to Recognize the Steady-State Visual Evoked Potentials with Wide Frequency Range
title_short The Empirical Mode Decomposition-Decision Tree Method to Recognize the Steady-State Visual Evoked Potentials with Wide Frequency Range
title_sort empirical mode decomposition-decision tree method to recognize the steady-state visual evoked potentials with wide frequency range
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6293644/
https://www.ncbi.nlm.nih.gov/pubmed/30603614
http://dx.doi.org/10.4103/jmss.JMSS_20_18
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