## 1 Introduction
This hypothesis underlines the importance of recording brain activity alongside exercise characteristics and environmental conditions, particularly under conditions of exhaustion. A better understanding of brain responses to exhaustive exercise up to \(\dot{\mathrm{VO}}_{2}\)max would rely on a multimodal approach that could combine neurological, physiological, and biomechanical data. Regarding neurophysiological aspects, two methods are less restrictive and allow body and head movements: near-infrared spectroscopy (NIRS) and electroencephalogram (EEG). Indeed, brain activity has already been studied during high-intensity exercise, notably when performing an IET on an ergobicycle until exhaustion using NIRS ([PERSON] _et al._, 2013; [PERSON] _et al._, 2013; [PERSON] _et al._, 2013) or EEG ([PERSON] _et al._, 2013; [PERSON] _et al._, 2013). EEG provides robust information on changes in cortical potentials, particularly rhythmic activity and frequency of synaptic processes ([PERSON] _et al._, 2013; [PERSON] _et al._, 2013). EEG is one of the most pragmatic means of monitoring changes in brain activity in humans during exercise, certainly because it is less intrusive ([PERSON] _et al._, 2013; [PERSON] _et al._, 2013; [PERSON] _et al._, 2013). EEG is used to measure cortical brain activity, which is categorized into distinct frequency ranges such as alpha (\(\alpha\): 8-12 Hz) and beta (\(\beta\): 12-30 Hz), each related to different cognitive functions regulated by the brain. Alpha activity, characterized by quite low-frequency oscillations, is associated with perceptual awareness and inhibition of non-essential processing, which facilitates task performance ([PERSON] _et al._, 2013; [PERSON] _et al._, 2013). Conversely, beta activity, characterized by high-frequency oscillations, is associated with voluntary contractions, alertness, and arousal, enhancing the perception of stimuli ([PERSON] _et al._, 2013; [PERSON] _et al._, 2013; [PERSON] _et al._, 2013). Spectral analysis is a common means of quantifying the frequency contentof these bands, the main result being the power spectral density (PSD), which indicates the strength or energy of variations between frequencies. An increase in oscillations in cortical regions yields to an increase in the EEG's spectral power. Several studies have coupled EEG recording with a breath-by-breath expired gas system [31; 33; 34]. EEG activity has generally been recorded during pedaling tests at a constant load without exhaustion [42; 43; 44; 45]. It has also been recorded during exercise to exhaustion in an incremental test [31; 32; 33; 34; 46]. While the role of the prefrontal cortex in exercise tolerance and termination has been investigated [47; 48; 49; 50] showing, for example, that the alpha/beta ratio decreased after the subject experienced exercise as \"hard\" in reference to the Rate of Perception of Exhaustion [51] and then above the RCP [33; 47; 48], no study has yet examined the relationship between the occurrence of VO2 pl with EEG response in well-trained athletes. To our knowledge, no study has yet examined the possible difference in brain activity during maximal incremental testing in VO2 pl and non-plateau athletes with the same VO2 max and age. This study aimed to shed light on the central governor hypothesis, namely that the inability to reach a VO2 pl may be dictated by the brain rather than by a peripheral physical limit. This hypothesis can now be explored using EEG during IET, measuring concomitant power in specific frequency bands. Forty-two athletes were divided into two groups: those who practiced endurance sports and those who did not, and were asked to perform an IET. We hypothesize that the difference between athletes who reached the plateau of their VO2 max and those who did not could be present in different EEG characteristics and in EEG/VO2 ratios. Specifically, we believe that athletes who do not achieve a VO2 pl will show a more pronounced decrease in the ratio between EEG power and VO2 as metabolic demand increases.