Given the hard problem of consciousness (Chalmers 1995, http://consc.net/papers/facing.html) there are no brain occipital (Oz) electrophysiological correlates of the subjective
experience (the felt quality of redness, the experience of dark and light, the quality of depth in a visual field, the sound of a clarinet, the smell of mothball,
bodily sensations from pains to orgasms, mental images that are conjured up internally, the felt quality of emotion, the experience of a stream of
conscious thought).
However, there are brain occipital (Oz) electrophysiological correlates of the subjective experience (Pereira 2015, https://philarchive.org/rec/PEROAL).
Notwithstanding, as evoked signal, the change in ERPs phase (frequency is the change in phase over time) is instantaneous, that is, the frequency will
transiently be infinite: a transient peak in frequency (positive or negative), if any, is instantaneous in EEG averaging or filtering that the ERPs required and
the underlying structure of the ERPs in the frequency domain cannot be accounted, for example, by the Wavelet Transform (WT) or the Fast Fourier
Transform (FFT) analysis, because they require that frequency is derived by convolution (frequency are pre-defined and constant over time) rather than by
differentiation (without predefining frequency and accounted that frequency may vary over time).
Despite that the Wavelet or the Fourier Transform are the methods most widely used for analysing the linear (proportionality or additivity) and stationary (the signal, and so the time series representing this signal, has the same mean and variance throughout) properties of the EEG signal, the EEG signal have nonlinear (nonproportionality or nonadditivity) and non stationary (signal's statistical characteristics change with time) properties.
However, one suitable method for analyse the instantaneous change in event-related brain potentials (ERPs) phase and accounted for a transient peak in frequency (positive or negative), if any, in the underlying structure of the ERPs is the Empirical Mode Decomposition (EMD) with post processing (Xie et al. 2014, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3946180/), Ensemble Empirical Mode Decomposition (postEEMD).
The Wavelet or the Fourier Transform analyse the signal in time-frequency-energy (Wavelet) and in frequency-energy (Fourier) domain without discrete feature extraction (Wavelet, with continuous feature extraction) or without discrete or continuous feature extraction (Fourier).
However, the Hilbert-Huang Transform (HHT) analyse the signal in time-frequency-energy domain for feature extraction.
For example, either the Fourier functions or the EMD functions are oscillations with zero mean derived from the decomposition of a signal (for example, ERPs) that when summed together reconstitute the original signal.
However, whereas the Fourier functions are called harmonic functions meaning that they amplitude and frequency are constant over time, the EMD functions are called Intrinsic Mode Functions (IMFs) meaning that they amplitude and frequency may vary over time.
Once the Intrinsic Mode Functions have been extracted and post processing (Xie et al. 2014, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3946180/), the Hilbert-Huang Transform can be used to display the underlying structure in the amplitude and frequency domain of the grand average left temporal electrical activity characterized in (Pereira 2015, https://philarchive.org/rec/PEROAL).