Comparison of FFT and marginal spectra by Hilbert-Huang transform for
broadband spectral analysis of EEG
Goal: Fast Fourier transform (FFT), has been the main tool for EEG
spectral analysis (SPA). As EEG can show nonlinear and non-stationary
behavior, FFT may at times be meaningless. A novel method was developed
for analyzing nonlinear and non-stationary signals using the
Hilbert-Huang transform. Methods: We compared spectral analyses of EEG
using FFT with Hilbert marginal spectra (HMS) with a multivariate
empirical mode decomposition algorithm. Segments of continuous 60-sec
EEGs recorded from 19 leads of 47 healthy volunteers were studied.
Results: HMS showed a reduction of the alpha activity (-5.64%), with
increments in the beta-1 (+1.67%), and gamma (+1.38%) fast activity
bands, an increment in theta (+2.14%), and in delta (+0.45%) bands,
and vice versa for the FFT method. For weighted mean frequencies,
insignificant mean differences (lower than 1Hz) were observed between
both methods for delta, theta, alpha, beta-1 and beta-2 bands, and only
for gamma band values. The HMS were 3 Hz higher than the FFT method.
Conclusion: HMS may be considered a good alternative for SPA of the EEG
when nonlinearity or non-stationarity may be present.