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

Sahar Sadeghi, Ali Maleki

DOI: 10.4103/jmss.JMSS_20_18


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 leadsto good results, but extending this range will seriously challenge the method so that even thecombination of IMFs is associated with error. Methods: Stimulation frequencies ranged from 6 to16 Hz with an interval of 0.5 Hz were generated using Psychophysics toolbox of MATLAB. SSVEPsignal was recorded from six subjects. The EMD was used to extract the effective IMFs. Twofeatures, including the frequency related to the peak of spectrum and normalized local energy in thisfrequency, were extracted for each of six conditions (each IMF, the combination of two consecutiveIMFs and the combination of all three IMFs). Results: The instantaneous frequency histogram andthe recognition accuracy diagram indicate that for wide stimulation frequency range, not only oneIMF, but also the combination of IMFs does not have desirable efficiency. Total recognition accuracyof the proposed method was 79.75%, while the highest results obtained from the EMD-fast Fouriertransform (FFT) and the CCA were 72.05% and 77.31%, respectively. Conclusion: The proposedmethod has improved the recognition rate more than 2.4% and 7.7% compared to the CCA andEMD-FFT, respectively, by providing the solution for situations with wide stimulation frequencyrange.


Brain-computer interface, decision tree classifier, empirical mode decomposition, frequency recognition, harmonic frequency, steady-state visual evoked potential

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