Brain oscillations in the alpha-band (8-14 Hz) have been linked to specific processes in attention and perception. In particular, decreases in posterior alpha-amplitude are thought to reflect activation of perceptually relevant brain areas for target engagement, while alpha-amplitude increases have been associated with inhibition for distractor suppression. Traditionally, these alpha-changes have been viewed as two facets of the same process.
View Article and Find Full Text PDFBackground: Prior work has shown that transcranial alternating current stimulation (tACS) of parietooccipital alpha oscillations (8-14 Hz) can modulate working memory (WM) performance as a function of the phase lag to endogenous oscillations. However, leveraging this effect using real-time phase-tuned tACS has not been feasible so far due to stimulation artifacts preventing continuous phase tracking.
Objectives And Hypothesis: We aimed to develop a system that tracks and adapts the phase lag between tACS and ongoing parietooccipital alpha oscillations in real-time.
Trait fatigues reflects tiredness that persists throughout a prolonged period, whereas state fatigue is a short-term reaction to intense or prolonged effort. We investigated the impact of sustained attention (using the SART) on both trait and state fatigue levels in the general population. An online version of the SART was undertaken by 115 participants, stratified across the whole adult lifespan.
View Article and Find Full Text PDFWhether prestimulus oscillatory brain activity contributes to the generation of post-stimulus-evoked neural responses has long been debated, but findings remain inconclusive. We first investigated the hypothesized relationship via EEG recordings during a perceptual task with this correlational evidence causally probed subsequently by means of online rhythmic transcranial magnetic stimulation. Both approaches revealed a close link between prestimulus individual alpha frequency (IAF) and P1 latency, with faster IAF being related to shorter latencies, best explained via phase-reset mechanisms.
View Article and Find Full Text PDFStatistical power in cognitive neuroimaging experiments is often very low. Low sample size can reduce the likelihood of detecting real effects (false negatives) and increase the risk of detecting non-existing effects by chance (false positives). Here, we document our experience of leveraging a relatively unexplored method of collecting a large sample size for simple electroencephalography (EEG) studies: by recording EEG in the community during public engagement and outreach events.
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