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Occipital and left temporal instantaneous amplitude and frequency oscillations correlated with access and phenomenal consciousness

Vitor Manuel Dinis Pereira

arXiv Preprint Archive December 26, 2020 via arXiv

Summary

AI-generated from the abstract

Brain activity patterns reveal how consciousness emerges! Scientists discovered specific brain regions that light up differently when we're consciously aware of something versus when information is processed unconsciously. Using advanced signal analysis, researchers found unique electrical patterns in the occipital and left temporal brain areas that correlate with conscious experiences, advancing our understanding of how the brain creates awareness.

Study at a glance

Characteristics Original research report Peer reviewed
Topics Philosophy of mind
Keywords Q-bio.nc Neuroscience Brain imaging Cognitive science
Key finding Empirical Mode Decomposition with post processing Ensemble Empirical Mode Decomposition and Hilbert-Huang Transform are suitable methods for analyzing the instantaneous change in event-related brain potentials phase and accounting for transient peaks in frequency.

Abstract

Given the hard problem of consciousness (Chalmers, 1995) there are no brain electrophysiological correlates of the subjective experience (the felt quality of redness or the redness of red, 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 or the phenomenology of thought). However, there are brain occipital and left temporal electrophysiological correlates of the subjective experience (Pereira, 2015). Notwithstanding, as evoked signal, the change in event-related brain potentials 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 electroencephalogram averaging or filtering that the event-related brain potentials required and the underlying structure of the event-related brain potentials 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 rather than by differentiation. However, as I show in the current original research report, one suitable method for analyse the instantaneous change in event-related brain potentials phase and accounted for a transient peak in frequency (positive or negative), if any, in the underlying structure of the event-related brain potentials is the Empirical Mode Decomposition with post processing (Xie et al., 2014) Ensemble Empirical Mode Decomposition (postEEMD) and Hilbert-Huang Transform (HHT).

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