The power of rhythms: how steady-state evoked responses reveal early neurocognitive development

Abstract

Electroencephalography (EEG) is a non-invasive and painless recording of cerebral activity, particularly well-suited for studying young infants, allowing the inspection of cerebral responses in a constellation of different ways. Of particular interest for developmental cognitive neuroscientists is the use of rhythmic stimulation, and the analysis of steady-state evoked potentials (SS-EPs) - an approach also known as frequency tagging. In this paper we rely on the existing SS-EP early developmental literature to illustrate the important advantages of SS-EPs for studying the developing brain. We argue that (1) the technique is both objective and predictive: the response is expected at the stimulation frequency (and/or higher harmonics), (2) its high spectral specificity makes the computed responses particularly robust to artifacts, and (3) the technique allows for short and efficient recordings, compatible with infants' limited attentional spans. We additionally provide an overview of some recent inspiring use of the SS-EP technique in adult research, in order to argue that (4) the SS-EP approach can be implemented creatively to target a wide range of cognitive and neural processes. For all these reasons, we expect SS-EPs to play an increasing role in the understanding of early cognitive processes. Finally, we provide practical guidelines for implementing and analyzing SS-EP studies.

Fig. 1.

Frequency decomposition and steady-state measurements. A – Time-domain representation of a simulated EEG signal, constructed as the sum of 5 pure sinusoids. B – The Fourier transform decomposes the signal into a set of pure sinusoids, each characterized by an amplitude A and an initial phase φ. Each time-resolved sinusoid, or spectral component, (on the left) can be represented as a complex number depicted in the complex plane (polar plots on the right). The amplitude of the sinusoid corresponds to the distance of the point from the origin (absolute value). The initial phase of the sinusoid corresponds to the angle of the point from the horizontal axis (argument). C – In the frequency domain, the power spectrum (top) represents the squared amplitude of each spectral component, and the phase spectrum represents the initial phase of each spectral component. D – Power spectrum of real EEG recordings, exhibiting a typical inverse power law distribution (1/f). A 6 Hz steady-state response is visible in the spectrum as a sharp narrow-band peak. Background noise is often quantified as the average activity over neighboring frequency bins. E – For each frequency, phase coherence captures the variance of the distribution of phases across epochs. Polar plots represent phase distributions at the stimulation frequency (in red, high phase coherence) and at a non-stimulated frequency (in black, low phase coherence). F – Examples of SNR (top) and ITC (bottom) plots.

Fig. 2.

Examples of infant SS-EP studies. A – Schematic patterns are contrast-modulated at a fixed rate (0.8 Hz), eliciting a steady state response in newborns. A significant difference between upright and inverted facelike patterns emerges in the power spectrum at 0.8 Hz over two clusters of electrodes (occipital and frontal). Adapted from Buiatti et al. (2019). B – Face (F) and object (O) stimuli are presented at 6 Hz to 4–6-month-old infants, with face stimuli appearing every fifth image, i.e. at 1.2 Hz. A clear peak associated to the face categorization response appears in the SNR plot at 1.2 Hz, over the right-lateralized electrode P8. Adapted from de Heering and Rossion (2015). C – Amplitude modulated sounds are constructed by varying the amplitude of a carrier signal (e.g. 500 Hz pure tone, in blue) using a rhythmic modulation signal (e.g. 25 Hz sinusoid in red). The strength of the auditory SS-EP amplitude varies as a function of modulation frequency. In infants, SS-EPs decrease with increasing frequency, while adult SS-EPs show a large enhancement around 40 Hz. Adapted from Stapells et al. (1988), with permission from Elsevier. D – Tri-syllabic non-sense words embedded in a continuous speech stream are presented to 8-month-old infants. The rhythmic presentation of syllables elicits a large steady-state response at syllable presentation rate. As infants progressively discover the systematic dependencies between the first and last syllables of the tri-syllabic words, another steady-state response emerges at word presentation rate. Adapted from Kabdebon et al. (2015).

Fig. 3.

Examples of SS-EP analysis pipelines. A – For a set of phase-locked epochs, EEG epochs are first Fourier-transformed, each yielding a set of spectral components. The polar plot represents the distribution of three spectral components (red, blue and black) across epochs, each characterized by a specific amplitude and phase. Each dot represents the spectral component computed from a single epoch, for a given frequency. Inter-trial coherence (ITC) can be computed at each frequency from these distributions. Alternatively, spectral components can be averaged across epochs before computing the power spectrum. In both cases, the SS-EP pops out as a sharp peak in the power or ITC spectrum. Signal-to-noise ratios can finally be computed to correct for the background noise. B – For a set of non-phase-locked epochs, SS-EPs can only be assessed using power-based measurements. After computing the Fourier transform and the power spectrum of each epoch, power spectra are averaged across epochs. The SS-EP is then visible as a sharp peak in the average power spectrum. Finally, the power spectrum can be normalized by some estimate of the background physiological noise, yielding a SNR value for each frequency.