Increases in cognitive fatigue are typically associated with extended periods of cognitive effort ( Trejo et al., 2005) and/or a lack of sleep ( Stuster, 2010): but what specifically causes cognitive fatigue to have a negative impact on performance? The negative consequences of cognitive fatigue have been attributed to reductions in action monitoring ( Kecklund and Åkerstedt, 1993 Campagne et al., 2004) attention ( Boksem et al., 2005), cognitive control ( Mizuno et al., 2011), decision-making ( Gaba and Howard, 2002), and error-evaluation ( Lorist et al., 2005) all of which add up to and result in reductions in performance. Indeed, increased cognitive fatigue results in increased errors and accidents while driving ( Fletcher et al., 2005), flying ( Goode, 2003), operating heavy machinery ( Tran et al., 2020), making medical decisions ( Cammu and Haentjens, 2012), and a wide range of other areas impossible to list here. For example, it is well established that cognitive (or mental) fatigue has a negative impact on brain performance ( Dinges et al., 1997 Lal and Craig, 2002 Borghini et al., 2014 Hopstaken et al., 2015 Trejo et al., 2015). The capability provided by mEEG to rapidly measure neural responses in situ provides a way to study factors that affect brain performance on a large scale. In addition, other research groups have demonstrated similar findings with low-cost EEG systems such as the OpenBCI Cyton ( Qiu et al., 2019) and the Emotiv Epoc+ ( Kotowski et al., 2019 Mercado-Aguirre et al., 2019). (2020) replicated our findings with the Muse EEG headband and also demonstrated that they could measure ERPs with this device. Importantly, the ERP results we recorded with the Muse EEG headband that were comparable to the ERPs we recorded with a “research grade” Brain Products ActiChamp system. Specifically, we demonstrated that we were able to measure the N200 and P300 ERP components – neural responses associated with the engagement of cognitive control and perceptual processing, respectively – using a Muse EEG headband ( Krigolson et al., 2017). Countering this uncertainty, in prior work ( Krigolson et al., 2017) we demonstrated that it was possible to record mEEG data that was comparable to that acquired by a research grade system. Since its advent, the scientific community has questioned the quality of mEEG data – especially of measurements collected by the growing array of low-cost (less than $1,000) mEEG systems. (2020) were able to record event-related potentials (ERPs) while participants were riding a bike. (2012) demonstrated that they could collect EEG data while participants were walking and more recently Scanlon et al. Over the past decade there has been a rapid increase in the use of mobile electroencephalography (mEEG) to address a range of research questions that have not been possible to ask with more tradition lab-based electroencephalographic (EEG) systems. In sum, our results provide validation of mEEG as a viable tool for research and provide further insight into the impact of cognitive fatigue on the human brain. Further, we demonstrate here that a linear combination of ERP and EEG features is a significantly better predictor of perceived cognitive fatigue than any ERP or EEG feature on its own. In line with previous findings we observed correlations between ERP components and EEG power and perceived cognitive fatigue. An analysis of our EEG data revealed robust N200 and P300 ERP components and neural oscillations in the delta, theta, alpha, and beta bands. Counter to traditional EEG studies, experimental setup and data collection was completed in less than seven minutes on average. To accomplish these goals, participants performed a standard visual oddball task on an Apple iPad while EEG data were recorded from a Muse EEG headband. As a secondary goal, we wanted to further demonstrate the capability of mEEG to accurately measure ERP and EEG data. To gain better insight into the neural signature of cognitive fatigue in the present study we used mEEG to examine the relationship between perceived cognitive fatigue and human-event related brain potentials (ERPs) and electroencephalographic (EEG) oscillations in a sample of 1,000 people. Cognitive fatigue – a neural state that is associated with an increased incidence of errorful performance – is responsible for accidents on a daily basis which at times can cost human lives. The advent of mobile electroencephalography (mEEG) has created a means for large scale collection of neural data thus affording a deeper insight into cognitive phenomena such as cognitive fatigue.
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