## 1 Introduction  
Self-pacing exercise allows individuals to regulate their exercise efforts according to subjective sensations and physiological states. This approach has been shown to improve adherence to exercise programs, particularly in certain populations, such as overweight and obese populations [1; 2], and this trend also extends to normal-weight individuals [3; 4]. The improved feelings of autonomy and enhanced positive affective responses contribute to exercise adherence. In addition, self-paced maximal exercise tests--where individuals regulate their pace during maximal exertion--have been shown to produce similar [5; 6] or even higher maximal oxygen uptake (\(\mathrm{\SIUnitSymbolMicro}\)2 max) values compared to traditional fixed incremental exercise tests (IETs). This suggests that giving individuals control over their pacing can optimize performance outcomes by allowing better management of effort and fatigue throughout the exercise [7; 8; 9]. However, despite these advantages, self-pacing requires careful application, particularly when working with less experienced athletes or when external feedback is limited. Ensuring that athletes maintain appropriate intensities is important to avoid potential risks of under- or overtraining [10]. With proper guidance and feedback, self-pacing can help athletes benefit from individualized intensity regulation while minimizing these risks. The Self-Paced VO2 max (SPV) protocol employs a progressive exercise design, where intensity increases are guided by the limits of ratings of perceived exertion (RPE), allowing individuals to self-regulate their pace. The RPE, developed by [PERSON], is a widely used subjective measure allowing individuals to rate their exertion levels during physical activity on a scale from 6 to 20 [11]. The RPE scale correlates well with physiological markers, such as heart rate (HR) and oxygen uptake (VO2), in healthy subjects, making it a reliable tool for prescribing and regulating exercise intensity [11; 12]. Recent research has further highlighted the utility of self-paced exercise, demonstrating that the RPE scale can effectively guide exercise intensity in various settings, including prolonged endurance events, like marathons [13]. This scale is an integral part of self-pacing, as it enables individuals to modulate their exercise intensity according to perceived exertion, thus aligning their physical effort with their subjective experience [14; 15]. The Central Governor Model proposed by [PERSON] and [PERSON] suggests that the brain plays a crucial role in regulating exercise performance by modulating effort perception and fatigue, further underscoring the importance of subjective measures like RPE in exercise science [16; 17; 18]. This theory is supported by recent findings showing early modifications in brain activity during prolonged exercise, even when cardiorespiratory responses remain steady [13]. This highlights the brain's critical role in managing exertion and preventing overexertion during endurance events, as indicated by the electroencephalography (EEG) and VO2 relationship, where EEG changes can precede observable drops in VO2, suggesting central regulation of fatigue. However, the literature still lacks proof of concept concerning the direct link between RPE and brain response during exhaustive exercise. Consequently, whether the brain acts as a limitation or, conversely, as a super controller for power optimization at the same RPE remains unknown. EEG is an effective method for assessing brain activity during exercise, providing high temporal resolution and enabling real-time monitoring of neural responses. Technological advancements have facilitated the use of EEG in dynamic exercise settings, reducing movement artifacts and enabling detailed analysis of cortical activity [19; 20; 21; 22; 23; 24]. EEG allows for the examination of how perceived exertion translates into measurable brain activity, linking subjective sensations with physiological responses [25; 26].