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.
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