Abstract
The average power of rhythmic neural responses as captured by M/EEG/LFP recordings is a prevalent index of human brain function. Increasing evidence questions the utility of trial/group averaged power estimates, as seemingly sustained activity patterns may be brought about by time-varying transient signals in each single trial. Hence, it is crucial to accurately describe rhythmic and arrhythmic neural responses on the single trial-level. However, it is less clear how well this can be achieved in empirical M/EEG/LFP recordings. Here, we extend an existing rhythm detection algorithm (“eBOSC”) to systematically investigate boundary conditions for estimating neural rhythms at the single-trial level. Using simulations and resting and task-based EEG recordings from a micro-longitudinal assessment, we show that rhythms can be successfully captured at the single-trial level with high specificity, but that the quality of single-trial estimates varies greatly between subjects. Importantly, our analyses suggest that rhythmic estimates at the single-trial level are reliable within-subject markers, but are not consistently valid descriptors of the individual rhythmic process. Finally, we discuss the utility and potential of rhythm detection, and various implications for single-trial analyses of neural rhythms in electrophysiological recordings.
Footnotes
Highlights
Extension of a state-of-the-art rhythm detection method (eBOSC).
Rhythm detection can offer specific indices of single-trial rhythmicity.
Arrhythmic duration systematically biases rhythmic power estimates.
Power- and phase-based definitions of rhythmicity derive similar estimates of rhythmic duration.
Surface EEG recordings exhibit stable inter-individual differences in a-rhythmicity.
Low levels of rhythmic strength constrain single-trial characterization of neural rhythms.
1 The eBOSC duration measure was further strongly correlated with the traditional Pepisode measure (estimated at the IAF) that results from the standard BOSC algorithm (EC: r = .96, p = 2e^-18; EC2: r=.94, p = 2e^-15; EO: r = .97, p = 3e^-20; EO2 = .97, p = 2e^-20), suggesting that both measures are similarly sensitive in our empirical data and reflect to a large extent overlapping information.
2 While a positive association may indicate an overestimation of the background amplitude in the presence of the rhythmic peak, it does not provide sufficient evidence for fitting problems as the two parameters may be naturally correlated.
