Prestimulus oscillatory neural activity has been linked to perceptual outcomes during performance of psychophysical detection and discrimination tasks. Specifically, the power and phase of low frequency oscillations have been found to predict whether an upcoming weak visual target will be detected or not. However, the mechanisms by which baseline oscillatory activity influences perception remain unclear. Recent studies suggest that the frequently reported negative relationship between α power and stimulus detection may be explained by changes in detection criterion (i.e., increased target present responses regardless of whether the target was present/absent) driven by the state of neural excitability, rather than changes in visual sensitivity (i.e., more veridical percepts). Here, we recorded EEG while human participants performed a luminance discrimination task on perithreshold stimuli in combination with single-trial ratings of perceptual awareness. Our aim was to investigate whether the power and/or phase of prestimulus oscillatory activity predict discrimination accuracy and/or perceptual awareness on a trial-by-trial basis. Prestimulus power (3–28 Hz) was inversely related to perceptual awareness ratings (i.e., higher ratings in states of low prestimulus power/high excitability) but did not predict discrimination accuracy. In contrast, prestimulus oscillatory phase did not predict awareness ratings or accuracy in any frequency band. These results provide evidence that prestimulus α power influences the level of subjective awareness of threshold visual stimuli but does not influence visual sensitivity when a decision has to be made regarding stimulus features. Hence, we find a clear dissociation between the influence of ongoing neural activity on conscious awareness and objective performance.

Transcranial direct current stimulation (tDCS) is a well-established technique for non-invasive brain stimulation (NIBS). However, the technique suffers from a high variability in outcome, some of which is likely explained by the state of the brain at tDCS-delivery but for which explanatory, mechanistic models are lacking. Here, we tested the effects of bi-parietal tDCS on perceptual line bisection as a function of tDCS current strength (1 mA vs 2 mA) and individual baseline discrimination sensitivity (a measure associated with intrinsic uncertainty/signal-to-noise balance). Our main findings were threefold. We replicated a previous finding (Giglia et al., 2011) of a rightward shift in subjective midpoint after Left anode/Right cathode tDCS over parietal cortex (sham-controlled). We found this effect to be weak over our entire sample (n = 38), but to be substantial in a subset of participants when they were split according to tDCS-intensity and baseline performance. This was due to a complex, nonlinear interaction between these two factors. Our data lend further support to the notion of state-dependency in NIBS which suggests outcome to depend on the endogenous balance between task-informative 'signal' and task-uninformative 'noise' at baseline. The results highlight the strong influence of individual differences and variations in experimental parameters on tDCS outcome, and the importance of fostering knowledge on the factors influencing tDCS outcome across cognitive domains.

Oscillatory neural activity is a fundamental characteristic of the mammalian brain spanning multiple levels of spatial and temporal scale. Current theories of neural oscillations and analysis techniques employed to investigate their functional significance are based on an often implicit assumption: In the absence of experimental manipulation, the spectral content of any given EEG- or MEG-recorded neural oscillator remains approximately stationary over the course of a typical experimental session (∼1 h), spontaneously fluctuating only around its dominant frequency. Here, we examined this assumption for ongoing neural oscillations in the alpha-band (8–13 Hz). We found that alpha peak frequency systematically decreased over time, while alpha-power increased. Intriguingly, these systematic changes showed partial independence of each other: Statistical source separation (independent component analysis) revealed that while some alpha components displayed concomitant power increases and peak frequency decreases, other components showed either unique power increases or frequency decreases. Interestingly, we also found these components to differ in frequency. Components that showed mixed frequency/power changes oscillated primarily in the lower alpha-band (∼8–10 Hz), while components with unique changes oscillated primarily in the higher alpha-band (∼9–13 Hz). Our findings provide novel clues on the time-varying intrinsic properties of large-scale neural networks as measured by M/EEG, with implications for the analysis and interpretation of studies that aim at identifying functionally relevant oscillatory networks or at driving them through external stimulation.