## 4 Discussion
In both cases, our multimodal device may allow fatigue to be monitored by several methods, to study the interactions between the central nervous system, the autonomic nervous system, and respiratory exchanges during acute exercise, which may provide information on exercise tolerance and regulation [25, 26, 49, 50]. Here, we attempted a multimodal approach providing different indicators of cortical activity changes that were synchronized with autonomic nervous system, gas exchanges, heart rate and behavioral performance while controlling body movements during an IETand comparing the EEG/VO2 profile according to the occurrence or non-occurrence of VO2 pl. * Impact of the cadence on the EEG responses between the NONPLAT and PLAT groups or NONEND and END ones during the IET  
As cadence has an impact on EEG and response during exercise [43; 94; 95], we allowed the participant to freely choose the pedaling frequency associated with a fixed power output. This should facilitate exercise, particularly when participants were not accustomed to pedaling on a cyclo-ergometer. In addition, changes in pedaling frequency can indicate the onset of fatigue or disengagement and can be objectively confirmed by physiological measures. Recording body movements should help to control the quality of EEG data by providing information that helps to distinguish the signal from movement noise. Thanks to the quality of the recordings, and even with a reduced number of electrodes (10 compared with the usual 20 to 64), a wide range of brain areas was covered. We were thus able to analyze the four frequency bands usually studied in sports science in the frontal, parietal and central regions of the brain: theta (4-8 Hz), alpha (8-12 Hz), beta (12-30 Hz), and low gamma (30-50 Hz) [31; 33]. As [PERSON] and colleagues suggest, EEG activity from previous research is difficult to compare due to specific differences in exercise protocol and apparatus [31]. For example, the site-specific density of electrical activity in the motor cortex was found to increase with exercise intensity until exhaustion [32]. Previously reported changes in EEG response to exercise showed that EEG activity varied during incremental or constant load exercise tests across all bandwidths [31; 33; 34]. Furthermore, it has been found that alpha activity in the motor cortex is not fully activated and is maintained during exercise exhaustion [50]. In contrast, alpha activity in the frontal cortex decreases from the second ventilatory threshold until exhaustion [33]. The frontal cortex could therefore play a central role in the cessation of exercise because of the changes it undergoes during high levels of physical exertion [50]. The precise cause of the effects of high-intensity exercise on cortical activity is difficult to determine because of concomitant physiological responses such as elevated body temperature and increased blood flow, which may influence oscillatory activity [84; 85]. Conversely, the effects of moderate-intensity exercise are less likely influenced by such factors because the increase in body temperature is not as pronounced [96]. The results of studies on moderate-intensity exercise have mainly revealed an increase in alpha and beta activity in different regions of the brain, which can be explained by the fact that alpha activity is linked to arousal [97] and beta activity plays a role in maintaining exercise at a steady state [98]. Analysis of the effects of low-intensity exercise on cortical activity has not revealed any significant trends, the most frequent observation being the absence of significant changes in the oscillatory bands, exception of the alpha band. The effects on alpha band activity vary, with some studies reporting an increase [32; 99; 100], others a decrease [101; 102; 103], and still others showing no change [104]. Like studies on moderate-intensity exercise, research on low-intensity exercise has focused mainly on the activity of the alpha band, limiting discussion of the other oscillatory bands.