Neural oscillations are ubiquitous across recording methodologies and species, broadly associated with cognitive tasks, and amenable to computational modeling that investigates neural circuit generating mechanisms and neural population dynamics. Because of this, neural oscillations offer an exciting potential opportunity for linking theory, physiology, and mechanisms of cognition. However, despite their prevalence, there are many concerns-new and old-about how our analysis assumptions are violated by known properties of field potential data. For investigations of neural oscillations to be properly interpreted, and ultimately developed into mechanistic theories, it is necessary to carefully consider the underlying assumptions of the methods we employ. Here, we discuss seven methodological considerations for analyzing neural oscillations. The considerations are to 1) verify the presence of oscillations, as they may be absent; 2) validate oscillation band definitions, to address variable peak frequencies; 3) account for concurrent non-oscillatory aperiodic activity, which might otherwise confound measures; measure and account for 4) temporal variability and 5) waveform shape of neural oscillations, which are often bursty and/or nonsinusoidal, potentially leading to spurious results; 6) separate spatially overlapping rhythms, which may interfere with each other; and 7) consider the required signal-to-noise ratio for obtaining reliable estimates. For each topic, we provide relevant examples, demonstrate potential errors of interpretation, and offer suggestions to address these issues. We primarily focus on univariate measures, such as power and phase estimates, though we discuss how these issues can propagate to multivariate measures. These considerations and recommendations offer a helpful guide for measuring and interpreting neural oscillations.

Neuronal oscillations emerge in early human development. These periodic oscillations are thought to rapidly change in infancy and stabilize during maturity. Given their numerous connections to physiological and cognitive processes, understanding the trajectory of oscillatory development is important for understanding healthy human brain development. This understanding is complicated by recent evidence that assessment of periodic neuronal oscillations is confounded by aperiodic neuronal activity, an inherent feature of electrophysiological recordings. Recent cross-sectional evidence shows that this aperiodic signal progressively shifts from childhood through early adulthood, and from early adulthood into later life. None of these studies, however, have been performed in infants, nor have they been examined longitudinally. Here, we analyzed longitudinal non-invasive EEG data from 22 typically developing infants, ranging between 38 and 203 days old. We show that the progressive flattening of the EEG power spectrum begins in very early development, continuing through the first months of life. These results highlight the importance of separating the periodic and aperiodic neuronal signals, because the aperiodic signal can bias measurement of neuronal oscillations. Given the infrequent, bursting nature of oscillations in infants, we recommend using quantitative time domain approaches that isolate bursts and uncover changes in waveform properties of oscillatory bursts.

Background: Corticospinal excitability depends on the current brain state. The recent development of real-time EEG-triggered transcranial magnetic stimulation (EEG-TMS) allows studying this relationship in a causal fashion. Specifically, it has been shown that corticospinal excitability is higher during the scalp surface negative EEG peak compared to the positive peak of µ-oscillations in sensorimotor cortex, as indexed by larger motor evoked potentials (MEPs) for fixed stimulation intensity.

Background: Corticospinal excitability depends on the current brain state. The recent development of real-time EEG-triggered transcranial magnetic stimulation (EEG-TMS) allows studying this relationship in a causal fashion. Specifically, it has been shown that corticospinal excitability is higher during the scalp surface negative EEG peak compared to the positive peak of µ-oscillations in sensorimotor cortex, as indexed by larger motor evoked potentials (MEPs) for fixed stimulation intensity.Objective: We further characterize the effect of µ-rhythm phase on the MEP input-output (IO) curve by measuring the degree of excitability modulation across a range of stimulation intensities. We furthermore seek to optimize stimulation parameters to enable discrimination of functionally relevant EEG-defined brain states.Methods: A real-time EEG-TMS system was used to trigger MEPs during instantaneous brain-states corresponding to µ-rhythm surface positive and negative peaks with five different stimulation intensities covering an individually calibrated MEP IO curve in 15 healthy participants.Results: MEP amplitude is modulated by µ-phase across a wide range of stimulation intensities, with larger MEPs at the surface negative peak. The largest relative MEP-modulation was observed for weak intensities, the largest absolute MEP-modulation for intermediate intensities. These results indicate a leftward shift of the MEP IO curve during the µ-rhythm negative peak. Conclusion:The choice of stimulation intensity influences the observed degree of corticospinal excitability modulation by µ-phase. Lower stimulation intensities enable more efficient differentiation of EEG µ-phase-defined brain states.