The impact of 1/f activity and baseline correction on the results and interpretation of time-frequency analyses of EEG/MEG data: A cautionary tale

Abstract

Typically, time-frequency analysis (TFA) of electrophysiological data is aimed at isolating narrowband signals (oscillatory activity) from broadband non-oscillatory (1/ f ) activity, so that changes in oscillatory activity resulting from experimental manipulations can be assessed. A widely used method to do this is to convert the data to the decibel (dB) scale through baseline division and log transformation. This procedure assumes that, for each frequency, sources of power (i.e., oscillations and 1/ f activity) scale by the same factor relative to the baseline (multiplicative model). This assumption may be incorrect when signal and noise are independent contributors to the power spectrum (additive model). Using resting-state EEG data from 80 participants, we found that the level of 1/ f activity and alpha power are not positively correlated within participants, in line with the additive but not the multiplicative model. Then, to assess the effects of dB conversion on data that violate the multiplicativity assumption, we simulated a mixed design study with one between-subject (noise level, i.e., level of 1/ f activity) and one within-subject (signal amplitude, i.e., amplitude of oscillatory activity added onto the background 1/ f activity) factor. The effect size of the noise level × signal amplitude interaction was examined as a function of noise difference between groups, following dB conversion. Findings revealed that dB conversion led to the over- or under-estimation of the true interaction effect when groups differing in 1/ f levels were compared, and it also led to the emergence of illusory interactions when none were present. This is because signal amplitude was systematically underestimated in the noisier compared to the less noisy group. Hence, we recommend testing whether the level of 1/ f activity differs across groups or conditions and using multiple baseline correction strategies to validate results if it does. Such a situation may be particularly common in aging, developmental, or clinical studies.

Dynamics of alpha suppression and enhancement may be related to resource competition in cross-modal cortical regions

“…Three blocks were resting-state: one with eyes open, one with eyes closed, and one with eyes open but wearing an eye-mask to block visual input. The resting state data were used for other purposes and are reported elsewhere ( Clements et al, 2021a ; Gyurkovics et al, 2021 ). The masked data were inconclusive 1 and 2 and are not described further.…”

“…Power values were baseline corrected using condition-specific subtractive baselining. We have previously shown that, compared to divisive baselining, subtractive baselining minimizes the potential of Type I errors that might occur due to the effect of the aperiodic, 1 /f component of power spectra ( Clements et al, 2021a , 2021b ; Gyurkovics et al, 2021 ). Baseline activity differs for the eyes open and eyes closed conditions, especially in the aperiodic 1/ f activity (as well as oscillatory activity), such that the eyes-closed condition has a greater 1/ f offset than the eyes-open condition ( Supp.…”

Recommendations and publication guidelines for studies using frequency domain and time‐frequency domain analyses of neural time series

“…Therefore, nonlinear transformations should not be applied to the raw observed power before separating the contributions due to each component, because this would lead to incorrect estimation of these two parameters. For example, one of the parameters could be systematically over‐ or under‐estimated depending on the value of the other (Gyurkovics et al, 2021). Several procedures are available to achieve this separation if one is interested in applying nonlinear transformations under the narrowband+broadband model choice (Clements et al, 2021; Donoghue et al, 2020; He, 2014; Hughes et al, 2012).…”

Midfrontal theta oscillations and conflict monitoring in children and adults

“…The minimum number of trials per experimental cell observed across participants was 13. Thus, a subsample of 13 trials per experimental cell and participant was randomly selected for 1 Recent work suggests that, when non-oscillatory 1/f background "noise" differs between groups, as may be the case across age groups, dB transformation of power may distort the magnitude of the observed effects, and other baselining methods (e.g., baseline subtraction) may be preferrable (Gyurkovics, Clements, Low, Fabiani, & Gratton, 2021). However, we elected to use dB transformation for the sake of ease of interpretation and comparability with previous studies.…”