Aperiodic EEG Predicts Variability of Visual Temporal Processing

Abstract

The human brain exhibits both oscillatory and aperiodic, or 1/f, activity. Although a large body of research has focused on the relationship between brain rhythms and sensory processes, aperiodic activity has often been overlooked as functionally irrelevant. Prompted by recent findings linking aperiodic activity to the balance between neural excitation and inhibition, we investigated its effects on the temporal resolution of perception. We recorded electroencephalography (EEG) from participants (both sexes) during the resting state and a task in which they detected the presence of two flashes separated by variable interstimulus intervals. Two-flash discrimination accuracy typically follows a sigmoid function whose steepness reflects perceptual variability or inconsistent integration/segregation of the stimuli. We found that individual differences in the steepness of the psychometric function correlated with EEG aperiodic exponents over posterior scalp sites. In other words, participants with flatter EEG spectra (i.e., greater neural excitation) exhibited increased sensory noise, resulting in shallower psychometric curves. Our finding suggests that aperiodic EEG is linked to sensory integration processes usually attributed to the rhythmic inhibition of neural oscillations. Overall, this correspondence between aperiodic neural excitation and behavioral measures of sensory noise provides a more comprehensive explanation of the relationship between brain activity and sensory integration and represents an important extension to theories of how the brain samples sensory input over time.

Keywords: aperiodic EEG; neural noise; temporal processing; visual perception.

Figure 1.

Experimental design and data modeling. Top, Illustration of the two-flash fusion task (see Materials and Methods). Bottom-left, Two-flash fusion accuracy is plotted against the ISI (average across participants). The thick blue line indicates the group average psychometric function fit; thin lines indicate single participants’ functions. The dotted red line indicates the threshold, and the shaded area indicates the function’ spread. Error bars indicate the standard error of the mean. Bottom-right, EEG spectrum of an example participant (channel Oz), where the solid blue line indicates the aperiodic fit, the dotted blue line indicates the periodic fit, and the dotted red line indicates the alpha frequency peak.

Figure 2.

Aperiodic activity across the scalp. Top, Distribution of aperiodic exponents in the EO and EC resting state (averaged across participants) over the scalp. Bottom-left, Topographic map of the difference between the aperiodic exponents of EC and EO data; red markers indicate significant channels (p < 0.05). Bottom-right, Differences in the aperiodic exponent of the EO versus EC resting state at channel Oz.

Figure 3.

Correlation between two-flash fusion accuracy and resting state EEG. Each row shows brain–behavior correlations between aperiodic exponents and age, alpha power, psychometric threshold, and slope in the EO (middle) and EC (right) condition. The scatterplot on the left shows the data and regression line (dotted line) for the channel with the greatest (absolute) correlation coefficient (black circle in the topographic maps). The topographic maps reflect the correlation coefficients; red markers indicate significant channels (p < 0.05).

Figure 4.

Spectral differences between participants with shallow and steep psychometric slopes. Left, Map of the t values across frequencies and electrodes; the white outline indicates significant differences (p < 0.05) after multiple-comparison correction. Middle, Comparison of the averaged EEG spectra of the two groups for channel Oz. Right, Topography of the t values averaged between 30 and 70 Hz. Red markers indicate electrodes with significant differences. The top row shows EO data; the bottom row shows EC data.