2021
DOI: 10.1111/ejn.15163
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EEG in motion: Using an oddball task to explore motor interference in active skateboarding

Abstract: For decades, the study of cognitive electrophysiology using electroencephalography (EEG) has taken place inside highly controlled research facilities as EEG signals are easily contaminated by a myriad of environmental factors (Luck, 2014).EEG research has informed our understanding of human attention, yet this knowledge generally comes from paradigms that isolate participants in faraday cages to avoid electromagnetic fields and other sources of noise that can compromise data quality (Puce & Hämäläinen, 2017). … Show more

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Cited by 15 publications
(13 citation statements)
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References 64 publications
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“…Overall, a decrease in power was found both in the MF condition and generally over time. These reductions in power are consistent with EEG in motion literature, where these decreases reflect an overload of incoming stimuli and a general state of cortical desynchronization during complex movements [ 55 ]. This further reduction between tasks might indicate that MF increases this desynchronization between brain regions.…”
Section: Discussionsupporting
confidence: 88%
“…Overall, a decrease in power was found both in the MF condition and generally over time. These reductions in power are consistent with EEG in motion literature, where these decreases reflect an overload of incoming stimuli and a general state of cortical desynchronization during complex movements [ 55 ]. This further reduction between tasks might indicate that MF increases this desynchronization between brain regions.…”
Section: Discussionsupporting
confidence: 88%
“…In the current project, accuracies did not differ significantly between types of movement. These results are in line with a previous study assessing CMI whilst skateboarding ( Robles et al, 2021 ). Our findings, however, deviate from other findings where accuracy was found to be reduced as movement complexity increased ( Reiser et al, 2019 , 2021 ).…”
Section: Discussionsupporting
confidence: 93%
“…Finally, we did not test the EV of reinforcement learning models in this proof of concept study. Future studies should test whether conventional computational models of cognition can make quantitative predictions about observable behavior in both simplistic lab-based experiments and in complex real-world tasks (Robles et al, 2021).…”
Section: Limitationsmentioning
confidence: 99%