Research on brain-behaviour relationships often makes the implicit assumption that these derive from a co-variation of stochastic fluctuations in brain activity and performance across trials of an experiment. However, challenging this assumption, oscillatory brain activity, as well as indicators of performance, such as response speed, can show systematic trends with time on task. Here, we tested whether time-on-task trends explain a range of relationships between oscillatory brain activity and response speed, accuracy as well as decision confidence. Thirty-six participants performed 900 trials of a two-alternative forced choice visual discrimination task with confidence ratings. Pre- and post-stimulus spectral power (1–40 Hz) and aperiodic (i.e., non-oscillatory) components were compared across blocks of the experimental session and tested for relationships with behavioural performance. We found that time-on-task effects on oscillatory EEG activity were primarily localised within the alpha band, with alpha power increasing and peak alpha frequency decreasing over time, even when controlling for aperiodic contributions. Aperiodic, broadband activity on the other hand did not show time-on-task effects in our data set. Importantly, time-on-task effects in alpha frequency and power explained variability in single-trial reaction times, and controlling for time-on-task effectively removed these relationships. Time-on-task effects did not affect other EEG signatures of behavioural performance, including post-stimulus predictors of single-trial decision confidence. Our results dissociate alpha-band brain-behaviour relationships that can be explained away by time-on-task from those that remain after accounting for it, thereby further specifying the potential functional roles of alpha in human visual perception.

Statistical 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. We collected data from 346 participants (189 females, age range 6-76 years) over 6 days, totalling 29 hours, at local science festivals. Alpha activity (6-15 Hz) was filtered from 30 seconds of signal, recorded from a single electrode placed between the occipital midline (Oz) and inion (Iz) while the participants rested with their eyes closed. A total of 289 good-quality datasets were obtained. Using this community-based approach, we were able to replicate controlled, lab-based findings: individual alpha frequency (IAF) increased during childhood, reaching a peak frequency of 10.28 Hz at 28.1 years old, and slowed again in middle and older age. Total alpha power decreased linearly, but the aperiodic-adjusted alpha power did not change over the lifespan. Aperiodic slopes and intercepts were highest in the youngest participants. There were no associations between these EEG indexes and self-reported fatigue, measured by the Multidimensional Fatigue Inventory. Finally, we present a set of important considerations for researchers who wish to collect EEG data within public engagement and outreach environments.