After first reductionistic approaches to understand the interactions of physical activity, neuronal control and cognitive performance, an increasing trend towards a more holistic understanding can be observed. A large number of research papers published in recent decades dealing with the influence of physical activity on cognitive performance (Chang et al., 2012; Cox et al., 2016; Crabbe and Dishman, 2004; Etnier et al., 1997) reflects the significance of this topic until today (Etnier and Chang, 2019; Sun et al., 2020). Here, research can be distinguished between effects regarding the actual cognitive performance output, from a psycho-behavioral point of view, and those related to (neuro-) physiological processes that are assumed to be underlying conditions of the observed behavior. While the cognitive performance output of physical activity is widely accepted, psycho-behavioral effects as well as underlying (neuro-) physiological processes and relationships are still not completely understood. Here, mainly studies covering three areas with specific tools for measurement can be differentiated. One area that can be associated with the connection of metabolism and vagal system applies heart rate variability (HRV) measurements. Analyzing HRV during exercise reveals information about the sympatho-vagal balance via the amount of interoceptive stimuli sent from the heart due to autonomic changes (Buchheit et al., 2007). The second area can be ascribed to the central nervous system and its connection to the vagal system measured by means of electroencephalography (EEG) frequency-band specific brain activity. Studies already indicate a connection of different EEG frequency bands to the autonomic nervous system (ANS) regulation (Ako et al., 2003; Ehrhart et al., 2000; Garcia et al., 2011; Kuo et al., 2016; Tang et al., 2009; Triggiani et al., 2016). Referring to neurophysiological analysis, EEG as a simple and mobile method to measure electrical brain activity in context of physical activity is often used (Bertollo et al., 2019; di Fronso et al., 2019; di Fronso et al., 2017; Park et al., 2015). Furthermore, it presents an applicable tool to achieve, e.g. via neuro-feedback, an enhancement of performance (di Fronso et al., 2017). For a brief overview of brain frequency bands and their functionality see (Malik and Amin, 2017). Also the determination of individual alpha peak frequency (iAPF) as a measure of information processing (Klimesch et al., 1996), attention (Mierau et al., 2017) and arousal (Jann et al., 2010; Lambourne and Tomporowski, 2010) is considered to be of interest in the context of performance improvement, e.g. in relation to psycho-behavioral processes. The third area can be assigned to the psychological level measured by means of the rate of perceived exertion (RPE). RPE is one of the main psycho-behavioral output control systems during exercise (St Clair Gibson et al., 2006) and defines an important factor for evaluation of internal load, e.g. for monitoring training (Impellizzeri et al., 2004; 2019).

Despite some commonalities, a major reason for partially contradictory results within these three branches of research can be seen in the type of analyzed movement, e.g. fine (Cuffaro, 2011) or gross motor skills (Esposito and Vivanti, 2013). Due to the higher influence of gravitational and inertial forces as well as due to the involvement of large muscle groups, gross motor movements are accompanied by different control mechanisms and by a clearly increased physiological strain, i.e. typically increased heart rate and associated changes in regulation of the ANS as well as in perceived exertion. How these different types of strain are interacting in context of gross motor movements is not completely clarified. We will focus on gross motor movements since they are more adequate to the majority of sports than fine motor movements. Further investigations of psychophysiological processes provoked through gross motor movements could support the elucidation of the interacting brain and cardiovascular system as well as performance outputs on behavioral level, which could be beneficial in the conception of therapy, rehabilitation and individual training.

A graded exercise test (GXT) signifies an applicable method to provoke exercise induced reactions in all three areas mentioned due to the increasing intensity until exhaustion. As the overview of GXT-conducted studies in Table 1 shows, to our knowledge, all these three areas have yet not been analyzed in parallel. Neither before and after nor during a GXT execution, HRV, EEG and RPE parameters were measured simultaneously. This pertains also for the concurrent assessment of only HRV and EEG. Thus, an interaction may only be inferred indirectly from results of previous studies investigating brain (Abeln et al., 2015; Bailey et al., 2008; Brümmer et al., 2011; di Fronso et al., 2019; Dykstra et al., 2019; Edwards et al., 2016; Gutmann et al., 2018; Jung et al., 2015; Mechau et al., 1998; Moraes et al., 2007; Robertson and Marino, 2015; Schneider et al., 2013), HRV (Banach et al., 2004; Candido et al., 2015; Cruz et al., 2017; da Silva et al., 2017; Duarte et al., 2014; Michael et al., 2017; Shiraishi et al., 2018; Yamamoto et al., 1992) and RPE (Duarte et al., 2014; Faulkner and Eston, 2007; Moraes et al., 2007) effects separately. Therefore, research interest rises to simultaneously study the reaction of the (neuro-) physiological system as well as the perceived exertion to a GXT execution.

Regarding the area of physiological effects induced by physical exercise associated to HRV analysis, one GXT study showed after the exercise mostly highly significant reductions of time (SDNN, RMSSD) and all spectral parameters (TP, LF, HF, LF/HF ratio) (Banach et al., 2004). The reduction of SDNN and especially RMSSD indicated a lower activity of the parasympathetic system, which is quite obvious as a consequence of an exhaustive exercise. Another study on HRV (da Silva et al., 2017) identified higher LF and lower HF power during the GXT compared with control condition, i.e. sitting motionless on a bicycle ergometer, portending an increased sympathetic heart rate activity. Referring to the recovery after a GXT, a HRV analysis has not yet been explicitly conducted, to our knowledge. Studies analyzing a steady strenuous physical activity revealed an increase of HRV frequency parameters during 30 minutes recovery towards pre-exercise level (Kaikkonen et al., 2008). But still there was a remaining difference to baseline values up to one hour after exercise (Spring et al., 2018) indicating no complete recovery of the autonomic cardiovascular system. All mentioned HRV studies show an alteration of the ANS during and after exercise.

Referring to the neurophysiological area, all studies collectively revealed an increase of brain activity power in the measured brain lobes as a consequence to GXT execution, despite some methodological differences. These differences are in e.g. GXT design, brain activity analysis, measured frequency bands (e.g. delta, theta, alpha, beta or gamma (only analyzed by (Robertson and Marino, 2015) and defined with 30-40 Hz frequency) and brain lobes (e.g. frontopolar, frontal, central, temporal, parietal or occipital lobe). Further studies (Dykstra et al., 2019; Mechau et al., 1998; Robertson and Marino, 2015) reported a decline of brain activity over the whole cortex, especially in the temporal and prefrontal cortex (PFC) at high intensities. Although several studies provided evidence for common tendencies, a concrete reproduction of results is according to the methodological differences (e.g. selection and time duration of GXT stages used for analysis) hardly to be identified. In addition, individual differences in the conducted GXT duration and performance were mostly neglected. Other studies revealed changes in the current density of the primary motor cortex (Brümmer et al., 2011) and the PFC (Abeln et al., 2015) with a direct relation to exercise intensity. Also a moderate positive correlation between PFC activity and heart rate (HR) was found (Abeln et al., 2015). Rest analysis after GXT was only investigated by two studies (Bailey et al., 2008; Mechau et al., 1998). Therein, pre-exercise level of general brain activity was reached after ten (Bailey et al., 2008) and fifteen minutes (Mechau et al., 1998). Regarding iAPF analysis, studies showed an increase of iAPF after GXT termination (di Fronso et al., 2019) with a prolonged effect until 10 minutes after exercise cessation (Gutmann et al., 2015; Gutmann et al., 2018) proposing an ANS regulation (di Fronso et al., 2019) and an improvement of perceptual and cognitive performance (Gutmann et al., 2018). In spite of some differences in EEG results, the majority of the studies suggest the brain activity manifestation most probably relies on peripheral physiology or central brain mechanism.

With respect to the area of psycho-behavioral measures, one study reported a strong increase of RPE from beginning to termination of the GXT (Faulkner and Eston, 2007) indicating high subjective perceived exertion probably as a consequence to cardiovascular exhaustion. Hereby, other potentially RPE influencing factors such as the core affect (di Fronso et al., 2020) or the focus of attention (Lohse and Sherwood, 2011) should be kept in mind. Concerning a connection to the neurophysiological system, a self-paced GXT study, regulated by RPE evaluation, examined a link of RPE to brain activity (Maceri and Cherup, 2019). In all measured parameters, an increase of prefrontal and central alpha as well as beta power was examined as a consequence to exercise conduction (Maceri and Cherup, 2019). Increased beta power in the prefrontal cortex (PFC) and motor cortex (MC) was explained to rely on a higher demand of planning and execution of movement during a GXT. Alpha power was linked to afferent feedback signaling regulated by PFC and also related to inhibited MC (Maceri and Cherup, 2019; Robertson and Marino, 2015).

For a comprehensive view of all three areas containing mostly effects that have been analyzed separately and were caused by exercise, this study aims to investigate the neurophysiological and cardiovascular as well as subjectively perceived strain of an exhaustive task by means of a GXT and their possible interactions in parallel. Therefore, not only the influence of a GXT on EEG brain activity, on RPE as well as on HRV is analyzed by comparing passive resting states prior and immediately after the GXT, but also during the exercise and in passive short-term recovery. According to the general statement of the presented state of research, we hypothesize an increase of brain activity (power of total spectrum and frequency bands: theta, alpha, beta, gamma) distributed over the whole cortex (temporal, parietal, occipital, particularly (pre)frontal and central lobe) as well as of iAPF, from baseline rest until rest directly after exhaustion. In contrast, HRV (LF and HF power) is expected to strongly decrease from beginning till exhaustion. RPE is assumed to strongly increase until GXT termination. Regarding recovery, we speculate a decreasing brain activity (power of total spectrum and frequency bands: theta, alpha, beta, gamma) over the whole cortex ((pre-)frontal, central, temporal, parietal, occipital lobe) and decreasing RPE, but an increasing HRV (LF and HF power) in direction to pre-exercise level (Kaikkonen et al., 2008). We also put forward the hypothesis of a negative correlation between brain activity (power of total spectrum and frequency bands: theta, alpha, beta, gamma) and HRV (LF and HF power) and hypothesize about a direct relationship of brain activity (power of total spectrum and frequency bands: theta, alpha, beta, gamma) and RPE.

Fifteen healthy male volunteers (23.6±2.7 years; 1.83±0.07 m; 78.6±8.9 kg) participated in this study. Based on the aspect that independent of sample size no generalization of the hypothesis to the population can be conducted, the applied sample size is sufficient to provide, according to Fisher’s statistics, results with enough basis to potentially foster research of similar studies (Gigerenzer, 2004; Nuzzo, 2014; Stegmüller, 1973). Only male participants with an age between 19 and 28 were recruited in order to control gender and age dependent effects on the brain (Wackermann and Matousek, 1998; Wada et al., 1994) and heart activity (Kuo et al., 1999; Umetani et al., 1998). All participants fit the neurologically necessary condition of the same handedness to compare brain activity (Serrien et al., 2006; T. Sun and Walsh, 2006). Right-handedness was selected as a criterion for study participation to facilitate acquisition of possible participants. Volunteers classified themselves as neurologically and cardiologically healthy and mentioned no related medical pre-existing conditions. Physical or cerebral activity influencing substances (Zschocke and Hansen, 2012), like coffee, alcohol or medication, have not been consumed at least 24 hours before the measurement. Subjects gave their written informed consent for study participation. None of the participants had prior experience of an incremental exercise test, but estimated to be able to perform a GXT. The process of a GXT was explained to the subjects before study beginning. Participants were coded with numbers for anonymity of personal data. Table 2 gives an overview of the included demographic and lifestyle variables. Compliance with the ethical standards took place under the conditions of the local institutional ethics committee. The study has been carried out in accordance with the Declaration of Helsinki (2013).

The study was conducted at the Sports Institute of the Johannes Gutenberg University of Mainz. With a within-subject design the effects of a GXT were investigated. EEG brain activity and ECG HRV as well as the Borg scale of perceived exertion (RPE) (Borg, 1982) were chosen as measurement parameters for (neuro-)physiological and subjectively perceived stress. Additionally, the grade of wellbeing, concentration and sleep was assessed via a classification of good, moderate or bad. The measurements were carried out under laboratory conditions. Changes in brightness and ambient noise were kept to a minimum and temperature was standardized (mean 21.2 SD 0.8°C).

The measurement procedure (Figure 1) started by identifying the current subjective state of every participant by means of the grade of wellbeing, concentration and last night’s sleep. Subsequently, at rest a measurement of spontaneous EEG activity with eyes open and ECG heart activity was conducted for five minutes just before the beginning of warming up for GXT. Subjects warmed up at 25W for three minutes. The GXT protocol was carried out on an individually adapted cycle ergometer until exhaustion under measurement of EEG and ECG activity as well as perceived exertion. Immediately afterwards, the recovery process was assessed sitting on a chair during 15 minutes with EEG brain and ECG heart activity measurement. Perceived exertion was rated after each increase of physical load and directly at exhaustion. Measurements before and after the GXT were conducted sitting on an immobile chair with eyes open and watching a smiley picture, fixed on head height in a few meters distance on the wall. Subjects were asked to sit comfortably, but also to minimize their head and eye movements.

The graded exercise test was performed on the electro-magnetically bicycle ergometer (Lode Excalibur - Lode B.V., Groningen, Netherlands). The protocol started with a resistance of 50W and an increase of 50W every 3 minutes until exhaustion (Huonker, 2004). Cycling cadence was prescribed with 60-70 rpm. Subjects were asked to keep movements of upper body and head to a minimum. Eyes were directed forward towards a smiley picture which was attached to the wall three meters away, at head height. Potential movement artefacts were tried to be restricted to a minimum. On the one hand the effects of seated cycling as an exercise with few vertical as well as horizontal whole-body movements were investigated. On the other, several methods of data cleansing were applied. The test was terminated by the second violation of the cadence interval, i.e. cadence below 60 rpm was only tolerated a single time. Furthermore the occurrence of exhaustion as well as stopping criteria (Dickhuth and Badtke, 2010) led to a termination of the GXT. No type of external motivation was given in order to prevent enhancing a subjects’ willpower, which could extend its stamina and result in a longer test duration till GXT termination.

The Borg rating of perceived exertion scale (RPE) (Borg, 1982) was applied to evaluate the exertion level during each stage of the GXT. At the end of every stage as well as at GXT termination subjects were asked to give their individual RPE shortly. Subjects read an instruction of RPE one day and directly prior to the measurement beginning to ensure reliable exertion output. A RPE of 6 was defined as no effort at all and a RPE of 20 as maximum effort ever experienced.

Spontaneous EEG was assessed during the whole test procedure by means of the EEG-system Micromed SD LTM 32 BS (Venice, Italy) with a sampling rate of 1024 Hz and recorded by the international 10-20 system using 19 electrodes, including Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1 and O2. EEG was recorded before and after the GXT at rest as well as during the exercise. For all EEG measurements a homogeneous and low impedance (<10kOhm) of the electrodes in all points was sought. The conduction of brain activity was unipolar with grounding on the nose. Furthermore, a two channel electrooculogram with electrodes at the medial upper and lateral orbital rim of the right eye was applied. Data was recorded by means of commercially available software (SystemPlus Evolution - Micromed, Venice, Italy).

ECG was assessed by means of an additional bipolar channel of the EEG-system Micromed SD LTM 32 BS with a sampling rate of 1024 Hz and recorded by the second lead according to Einthoven. One electrode of the second Einthoven limb lead was placed on the right side of the body, lateral, directly below the clavicle and the other on the left body side, lateral, directly below the costal arch to reduce artefacts during movement. ECG was recorded during the whole test procedure by means of the software SystemPlus Evolution (Micromed, Venice, Italy). To reduce artefacts as a result of body or ECG cable movement during exercise, the upper body of participants was dressed up with a compressive net tubing.

Throughout the analysis, a statistical significance level of five percent (p ≤ 0.05) according to Fisher statistics was determined. Calculation of effect size was based on Neyman-Pearson statistics (Neyman and Pearson, 1933) and results were presented by Cohen’s d (Cohen, 1988): d < 0.5 (small effect), d < 0.8 (medium effect), d > 0.8 (large effect). According to correlation analysis, the correlation coefficient r was interpreted as followed: r ≥ 0.1 (small relation), r ≥ 0.3 (moderate relation), r ≥ 0.5 (strong relation) (Cohen, 1992). Data of the recorded measurements of brain activity were processed by means of MATLAB-based software EEGLAB 14_1_1b (MathWorks, USA; Swartz Center of Computational Neuroscience, San Diego, USA). Data of ECG, specifically HRV were computed by means of MATLAB-based software Kubios HRV 2.2 (Finland). Data of the recovery process was divided into three five-minute intervals. Processed data and all the other measured data were entered into the software SPSS 23 (IBM, USA) for statistical analysis. All conducted statistical tests were non-parametric to minimize possible influence of artefacts due to body or head movement. Based on rank scaling, the interference of possible outliers in data analysis is reduced (Whitley and Ball, 2002). Data processing and analysis differed between rest and exercise measurements (Figure 1). Descriptive statistics were generated for every sub-region of analysis (Table 3).

Via the correlation of Spearman, an interaction of RPE and EEG as well as HRV parameters during the GXT was analyzed.

The output of the variables general well-being, sleep of the previous night and current concentration were transformed into a frequency scale.

A basic finite impulse response (FIR) filter was used to bandpass data (0.1-120 Hz). Furthermore, a notch filter (50 Hz) was applied. For analysis of brain activity recorded during the GXT, only the last 90 seconds of every three-minute stage were taken to minimize possible influence of adaption processes due to the change of load. Spectral analysis was used as assessment and interpretation method of EEG data (Zschocke and Hansen, 2012). Relevant frequency parameters were the theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz), gamma (30-70 Hz) band and the total spectrum (4-70 Hz) (Zschocke and Hansen, 2012). For each frequency band and total spectrum, mean power spectrum of the EEG signal was created by discrete Fast-Fourier-Transformation with a window size of 2 seconds and 75% window-overlap. Interference-prone channels were interpolated. Furthermore, an independent component analysis (ICA) (Makeig et al., 1996) was conducted. Recurring artefacts, such as eye closing, eye movement and muscular artefacts were filtered by reducing interference-prone components. After visual inspection of the complete recordings, individually occurring abnormal interferences of the electric potential, like body movements or sweat artefacts, were eliminated out of the data length. Average data reduction was approximately 10% of each data file length, i.e. 9.5 (SD 5.4) seconds of each data file during the GXT and 30.5 (SD 18.3) seconds of each data file at rest. Power spectrum of the electrodes were averaged across their cerebral areas, i.e. FP1 and FP2 for the frontopolar lobe, F7, F3, Fz, F4 and F8 for the frontal lobe, C3, Cz and C4 for the central lobe, T3, T4, T5 and T6 for the temporal lobe as well as O1 and O2 for the occipital lobe. Furthermore, data of all lobes were averaged to gain information about the total brain power (total power, TP). Average iAPF of all lobes was determined by the mean center of gravity of all channels calculated by an automated iAPF estimation method (Corcoran et al., 2018). For power spectral density estimation via pWelch, a Hamming window with 4 seconds length and 50% overlap was used. The minimum number of channel-estimates necessary for computing cross-channel averages was set to 1. The applied Savitzky-Golay filter for improving spectral peak detection was defined by a frame width of 7 and polynomial order of 5. EEG data of rest measurements was normalized by defining Pre-values as baseline (100%) and presenting post-rest values relative to Pre-values (%Pre) by dividing the specific Post- through Pre-values. According to different data length between data of recovery and GXT, data during the exercise was normalized to the first stage of the GXT (50W) and expressed as a relative (%50W) by dividing each GXT stage value through the 50W-value. As a consequence, data of 50W, i.e. presenting 100% of %50W, was excluded from correlation analysis. To minimize the influence of noise and according to a rather small number of measurements, the median was used for further analysis of spectral power.

For statistical examination, the Friedman test including Dunn-Bonferroni tests for post hoc analysis (comparison of rest measurements), as well as Spearman correlation were used due to nonparametric data and to reduce the effect of possible outlier measurement data. To determine a possible interaction between brain activity and HRV parameters as well as RPE, several correlations with differing measurement data were conducted. One correlation analysis included data only measured during the GXT (data of the completed GXT stages transformed in dependency of relative HRmax) and the other was only related to data of the recovery process (Post5, Post10, Post15 data).

HRV-data was bandpass filtered by a basic FIR filter (0.1-120 Hz) as well as notch filtered (50 Hz). HRV was used as assessment and interpretation method of ECG data. For analysis the second lead (electrode below the right clavicle to electrode below the left costal arch) of the Einthoven triangle was used according to most clear signal characteristics and least artefacts. Via Kubios HRV, ECG data was inserted and computed for the time intervals of the test procedure. First, correct RR detection was proofed via visual inspection of ECG raw signal and incorrect or missing detection was adjusted. Artefact correction tool was used to eliminate artefacts of RR data. Over all measurements, artefact reduction was <10% of total data. Detrending of RR data was conducted applying the method Smooth Priors with λ = 380, resulting in a cut-off frequency of 0.040 Hz (Tarvainen et al., 2002). Frequency analysis was calculated via Fast Fourier Transformation with Welch’s periodogram method and interpolation rate of 4 Hz, window-width of 120 s and window-overlap of 75% for rest measurements and window-width of 40 s and window-overlap of 75% for GXT data. HRV data of rest measurements was normalized by defining Pre-values as baseline and presenting post-rest values relative to Pre-data (%Pre). According to different data length between data of recovery and GXT, data during the exercise was normalized to the first stage of the GXT (50W) and expressed as a relative (%50W).

Time domain (Mean RR, Mean HR, SDNN, RMSSD), frequency domain of low frequency (LF, 0.04-0.15 Hz) and high frequency (HF, 0.15-0.4 Hz) power (Shaffer and Ginsberg, 2017) as well as ECG derived respiration (EDR) (Cysarz et al., 2008) were analyzed. Mean RR is defined as the arithmetic mean of the time between two consecutive R-peaks of the ECG signal. SDNN, standard deviation of normal sinus beat intervals, is an indicator of sympathetic (SNS) and parasympathetic nervous system (PNS) activity. It correlates with LF power (Umetani et al., 1998). RMSSD stands for root mean square of successive differences between normal heartbeats and informs about the beat-to-beat variance in HR and is typically taken for the evaluation of the PNS influence on HRV changes. It correlates with HF power (Kleiger et al., 2005). LF power is defined as the power of HRV-activity with a frequency of 0.04 to 0.15 Hz and is mainly associated with the activity of the SNS and PNS. HF power reflects primarily the PNS activity. EDR represents an easy applicable estimate of respiratory frequency, particularly reliable in context with higher levels of physical activity (Cysarz et al., 2008). With information about the respiratory rate, interpretation of ANS regulation, specially HF power changes may be facilitated (Cysarz et al., 2008).

To facilitate the presentation of results and minimize the amount of calculated HRV parameters, a principal component analysis was conducted. Additionally, the knowledge about existing correlations between several HRV parameters helped to focus on the most relevant HRV parameters. Thus, presentation of HRV parameters was reduced to LF power, HF power and EDR.

The Friedman test including Dunn-Bonferroni tests for post hoc analysis was applied for statistical examination of effects between rest measurements. Via the correlation of Spearman, HRV and EEG parameters were analyzed for linear interactions during post-rest measurements and furthermore during the GXT. To normalize subject dependent, different performance level and time of the GXT, maximal heart rate (HRmax) of each subject was determined and mean HR of each stage was represented as a relative of the HRmax. Hence, all other HRV as well as EEG and RPE parameters were presented in dependency of relative HRmax. This data processing was also conducted with the data of recovery.

Table 3 displays the descriptive results of selected variables.

Before starting the experiment, grade of well-being was rated in 87% of the cases as good and 13% as moderate. Last night’s sleep was evaluated nearly equally distributed between moderate 53% and good 47%. Subjects declared to 80% a good and to 20% a moderate ability to concentrate.

Statistical analysis revealed EEG frequency band dependent effects in pre- to post-rest comparison (Table 4). In the total spectrum and in each frequency band, there was a global significant effect in the temporal lobe. Post hoc analysis identified significant effects between pre- (Pre) to the first post-rest (Post5) and between pre- and the second post-rest (Post10) measurement in the temporal lobe. Additionally, alpha, beta and gamma frequency bands showed significant effects in comparison of the pre- and the last post-rest measurement (Post15). In the frontopolar lobe, theta, alpha and beta frequency was significantly different over all rest measurements and in post hoc-test comparing Pre and Post5. Only in alpha and beta frequency, post hoc comparison of Pre and Post10 led to significant effects. According to the frontal lobe, there were global significant effects in the alpha and beta frequency band, but only alpha frequency showed significant effects in post hoc comparison of Pre to Post5 and Post10. Regarding iAPF analysis, 135 out of 139 possible iAPF estimations could be extracted in accordance with the detection criteria (Corcoran et al., 2018). This resulted in an exclusion of maximal two subjects in the calculation of descriptive and inference statistics (comparison of pre- and post-rest measurements). Analyzing the pre- and post-rest measurements led to no significant difference in iAPF.

Statistical analysis of HRV revealed global significant effects in all parameters in pre- to post-test comparison (Table 5). Post hoc analysis led to parameter dependent differences. Related to LF power, there were significant effects between Pre and Post5 as well as Post10, and between Post5 and Post15. HF power showed significant effects from Pre to each post-rest measurement. EDR significantly differed between Pre and Post5.

To investigate a possible relationship of EEG, HRV, and RPE parameters, correlation analysis of the different data sources was calculated during the exercise as well as during recovery. Correlation analysis revealed an EEG frequency band and location dependent relation between power spectrum and HRV as well as RPE (Table 6 and Table 7). According to increasing load, there were related changes of the main measured parameters (Figure 2).

Furthermore, correlation analysis of data during the GXT (Table 6) resulted in mainly moderate negative effects of total spectrum and beta as well as gamma frequency in total power and the frontopolar, temporal and occipital lobe with LF power (Figure 3). Regarding a correlation with HF power, the total power and the occipital lobe showed moderate negative correlations in beta and gamma frequency as well as total spectrum.

Considering RPE and EDR, only temporal gamma power led to a moderate positive correlation (Figure 3). iAPF revealed no moderate correlation to HRV parameters and RPE, but in relation to specific brain frequency bands. A moderate negative correlation of iAPF with theta and alpha frequency in the frontal, central lobe as well as in the total power and a moderate positive correlation of the temporal gamma activity was identified (Figure 4).

Concerning the recovery after the GXT, there were clearly fewer moderate correlations between brain activity and HRV (Table 7). Solely, temporal power of the total spectrum, beta and gamma frequency revealed moderate negative correlations with HF power (Figure 5). The iAPF only correlated moderately negative with frontal theta power (Figure 4).

This study aimed to investigate the effects of an exhaustive physiological demand on the (neuro-) physiological system as well as the subjectively perceived exertion and their interaction. The influence of a GXT, performed on a cycle ergometer, on EEG brain activity, HRV as well as RPE was analyzed by comparing resting states prior and directly after the exercise and by correlation analysis during the exercise and in short term recovery. According to the state of research, we hypothesized an increase of brain activity (power of total spectrum and frequency bands: theta, alpha, beta, gamma) over the whole cortex (temporal, parietal, occipital lobe, particularly (pre)frontal and central lobe) from baseline rest to rest directly after exhaustion. HRV (LF and HF power) was expected to strongly decrease from beginning until exhaustion. RPE was assumed to strongly increase during the GXT. Referring to the recovery process, it was speculated that total brain activity (all measured frequency bands in all lobes) decreases and HRV (LF and HF power) increases in direction to pre-exercise level. With regard to a correlation analysis, we hypothesized an inverse relation of total brain activity and HRV as well as a positive relation of total brain activity to RPE. The correlation analysis was conducted with data measured during the GXT and with data measured during the recovery process. The results comparing pre- and post-exercise rest measurements are discussed prior to the results during the GXT.

Concerning HRV, there were highly significant global effects of all frequency parameters as well as a significant global effect in EDR in comparison to rest measurements. As expected by earlier research (Banach et al., 2004), LF and HF power of post hoc analysis highly significantly decreased as a consequence of the GXT, i.e. pre- to first post-rest comparison (Pre-Post5). In the same context, EDR led to a significant increase indicating a raised respiratory rate. Thus, the existence of strong physiological strain and a connected regulation of the ANS evoked by the GXT can be assumed.

Regarding brain activity, the analysis of all rest measurements (Pre, Post5-Post15) revealed a global significant effect of EEG power in total and in each frequency band in the temporal lobe. Frontopolar, there were significant global effects in the theta, alpha and beta band, but not in gamma. Alpha and beta activity differed in general significantly in the frontal lobe. Referring to iAPF analysis, no significant global effect was determined. This result is in contrast to significant increased iAPF after an exhausting task examined by other studies (Gutmann et al., 2015; Gutmann et al., 2018). But our results show a similar trend, i.e. a slight increase after the GXT and further increase till 10 minutes of recovery. According to present interindividual differences in iAPF (Mierau et al., 2017), differing results could be based on a missing baseline normalization in the other studies. Concerning the acute effect of GXT (comparison of Pre and Post5), post hoc analysis showed a significant increase in the whole temporal spectrum, in frontopolar theta, alpha and beta power as well as frontal alpha power. No significant changes either in the total spectrum power or in the central, parietal and occipital lobes were found. Thus, the assumption and hypothesis of an overall power increase (Bailey et al., 2008; Mechau et al., 1998; Robertson and Marino, 2015) could not be confirmed with the data underlying this study. Thus, a frequency and lobe specific interpretation of results was indicated. But, referring to the applied EEG system and analysis method, an interpretation that is specific to brain areas has to be handled with caution in general (Michel and Brunet, 2019). The significant increase of temporal total spectrum was not observed so far by other GXT studies. Only one study (Mechau et al., 1998) described changes in the temporal lobe, but only a reduction of beta and alpha1 power in the shift from moderate to high intensity load. Also, temporal power changes were unexpected due to the absence of any acoustical or other sensory motivation stimuli. However, this change could point to raised working memory processes (Malik and Amin, 2017), increased attention as well as multisensory and sensorimotor integration (Jensen et al., 2007). Conducting a physical activity, e.g. from low intensity to exhaustion, may require a permanent processing of differing sensory stimuli sent from the inner body (e.g. ANS, muscular system, psychological system) and from the environment (e.g. control of cadence compliance) (Davranche et al., 2015; Furley and Wood, 2016; Kayser, 2003; McMorris et al., 2015; Tomporowski, 2003). Concerning working memory, medial temporal lying hippocampus with its memory related functions may have been provoked through physical exercise (Voss et al., 2020). Additionally, higher working memory was identified after physical exercise (Tsujii et al., 2013; Voss et al., 2020), particularly at higher intensity (Chang et al., 2013). Relating to the Working Memory Model (Baddeley, 2000), the Central Executive may be demanded to regulate attention to the incoming important stimuli and to disregard others. Furthermore, the provoked temporal power could be evidence for possibly provoked emotional processes in the inner temporal lobe lying amygdala (Kolb and Whishaw, 2009; Ray and Cole, 1985), in context to a GXT execution like ambition, mental fortitude or perseverance. Here of, increased amygdala activation was examined as a consequence of increased exercise intensity (Robertson and Marino, 2016). Furthermore, activation of the amygdala seems to rely on the PFC inhibiting visceral interoceptive responses associated with HRV (Sakaki et al., 2016). The increase of frontopolar and frontal activity could suggest a provoked PFC. Related to the frontopolar lobe, top-down control of executive functions like selective attention, self-control and -regulation could be provoked as a consequence of maintaining goal-directed behavior as demanded by a GXT execution (Robertson and Marino, 2015). Additionally, the prefrontal cortex is most often linked with the control of emotional behavior and basic drives essential for fundamental viability like hunger, thirst, but also physical activity (Fuster, 2001). The revealed higher frontopolar beta power is in accordance with prior studies (Moraes et al., 2007; Robertson and Marino, 2015). The increase of beta power could be interpreted as higher cortical activation (Cheron et al., 2016; Moraes et al., 2007) and augmented arousal level (Abhang et al., 2016) as well as indicating an enhanced attentional demand (Nielsen and Nybo, 2003). The increment in frontopolar theta activity could support the assumption of increased top-down control of executive functions as a consequence of GXT conduction (Cavanagh and Frank, 2014; Robertson and Marino, 2015). The increased frontopolar and frontal alpha power is comparable to prior results (Bailey et al., 2008; Edwards et al., 2016; Mechau et al., 1998; Robertson and Marino, 2015). Frontal alpha power seems to promote information processing (Robertson and Marino, 2015) and be related to enhanced internal mental activity (Stein and Sarnthein, 2000) as well as attention (Cheron et al., 2016).

In view of the recovery process, a noteworthy cardiovascular recuperation after the GXT was confirmed due to increasing frequency-domain parameters dependent on recovery duration. Supporting this, LF power showed also a significant difference between the first and last post-rest measurement (Post5-Post15). Regarding the comparison to pre-rest, highly significant reductions in LF and HF power were calculated up to ten minutes after GXT termination (Pre-Post10). 15 minutes after exhaustion, only HF power stayed significantly reduced to pre-rest condition. EDR only showed a significant increment until Post5. Therefore, a probably prolonged blocked parasympathetic activity independent of respiratory rate as a consequence of exhaustion is hypothesized. In contrast to HRV, EEG power analysis of recovery showed a mostly decreasing trend, but without a significant effect on brain activity between post-rest measurements. Compared to pre-rest, significant differences in all above mentioned global EEG effects up to 10 minutes after exhaustion (Pre-Post10) were revealed. 15 minutes after exhaustion, only temporal alpha, beta and gamma frequency stayed significantly different from baseline. This could be interpreted as still provoked attention and working memory processes as well as existing emotional processes supporting the hypothesis of an enormous (neuro-)physiological strain and somatosensory demand (Herrmann et al., 2010) due to a GXT. Comparing the recovery process of HRV and brain activity, pre-exercise level of HRV parameters were far from being reached during 15 minutes of recovery. EEG results point towards almost complete recovery indicating a probably shorter recuperation time of brain activity than of the ANS. Referring to the still prolonged influence on HF power of HRV and temporal lobe EEG-activity, a possible reciprocal connection could be implied.

To support the hypothesis of a negative relationship between total EEG spectral power and HRV (LF and HF power), correlation analysis was used in one case of data measured during the GXT and in another case of data measured during the recovery process. To facilitate the interpretation of results, only at least moderate correlations are discussed. Correlation analysis during the GXT revealed moderate effects of total spectrum power with LF and HF power, but validity of results could be impaired according to a possible impact of pooled data. Nevertheless, these results of total power could support the hypothesis that through exercise induced widespread changes in EEG brain activity are caused by peripheral physiology (Kraaier et al., 1988; Kraaier et al., 1992) and metabolism processes (Mechau et al., 1998). Regarding the correlation analysis in greater detail, next to the total spectrum particularly higher frequencies such as beta and gamma activity registered mostly the highest correlation effects with HRV parameters. Beta frequency is suggested to play a central role in the processing of sensorimotor information (Cheron et al., 2016) and be provoked at increasing emotional arousal (Aftanas and Golocheikine, 2001). The presence of both aspects could be suggested as a consequence of an exhausting exercise. A link of beta frequency with cardiovascular strain, as the correlation effects indicate, could be supposed. Referring to gamma frequency, a direct connection of somatosensory demand to cardiovascular strain may be existent. Brain lobe specific, LF power correlated moderately negative with the frontopolar, temporal and occipital lobe. Furthermore, the total power as well as the occipital lobe showed mostly moderate negative effects with HF power. In context with EDR and RPE, solely temporal gamma activity led to a moderately positive effect. The moderate negative correlation between LF power and frontopolar gamma power hypothesize that the level of working memory (Jensen et al., 2007) and attention processes (Herrmann et al., 2010) in context with executive functions are directly connected with a regulation of the ANS. Additionally, PFC related top-down control of executive functions, provoked by physical exercise (Robertson and Marino, 2015), seems to increase with higher physiological demand. Furthermore, the PFC linked regulation of emotional behavior and basic drives related to physical exercise (Fuster, 2001) may be dependent of cardiovascular strain level.

Referring to the temporal lobe, the correlations may be in line with the assumptions already determined by pre- and acute post-exercise comparison. Evidence exists for a negative interaction of HRV frequency parameters with multisensory and sensorimotor integration (Jensen et al., 2007), working memory (Chang et al., 2013; Tsujii et al., 2013; Voss et al., 2020) and emotional processes (Robertson and Marino, 2016; Sakaki et al., 2016). Concerning the highest correlation of the temporal gamma power with RPE and the certain connection of the posterior cingulate cortex to the temporal lobe, the assumed functionality of the posterior cingulate cortex as a responsible structure for assessing perceived exertion could be supported (Fontes et al., 2015). The moderate correlation of EDR and temporal gamma power supports the existing relation of gamma activity (Herrero et al., 2018) and temporal lobe functions (Zelano et al., 2016) with changes in the respiratory rate. Referring to the occipital lobe with similar correlation effects to HRV, a provoked multisensory demand may be indicated including additional stress in visual processing. Visual processing appears to raise with increasing exercise intensity, though no specific, differing visual stimuli was given in GXT progression. However, this effect could be related to the instruction to focus on a picture on the wall during the GXT. Due to increasing exercise intensity, enhanced body movement is provoked which probably makes it more difficult to focus on the picture and minimize head movements. Therefore, interpretation of occipital effects due to pure cardiovascular strain has to be taken with caution. The highest negative correlation of HF power with the occipital lobe and temporal lobe, next to total power, may be interpreted that provoked multisensory processing is linked to reduced parasympathetic activity in context with an exhausting exercise. Summarizing the results and the interpretation of temporal and frontopolar correlations with HRV frequency domain, an increased connectedness of these lobes during physical exercise may be speculated. Considering the occipital lobe, further research of a potential influence of the visual stimulus during a GXT has to be conducted. Related to iAPF, no moderate correlations to HRV and RPE could be identified. Only in context with brain activity, a moderately negative correlation with EEG theta and alpha total power, even more particularly in the frontal and central lobe, and a moderately positive correlation with temporal gamma power could be found. These relations can be explained as followed: Increased iAPF is associated with increased allocation of attentional resources and somatosensory input (Mierau et al., 2017) as well as interpreted with higher information processing (Klimesch et al., 1996). Referring to frontal and central theta power, frontal midline theta power, as an indicator of top-down sustained attention is understood to be lower in context with optimal attentional engagement and higher with excessive attentional control (Kao et al., 2013). According to the negative correlation of theta power and iAPF, we hypothesize that during a GXT the psychophysiological effects related to an iAPF increase occur by means of a more efficient application of attentional resources. Furthermore, alpha power seems to play an important role in top-down anticipatory attention such that reduced alpha power indicate enhanced cortical excitability in task relevant areas (ElShafei et al., 2020), e.g. the frontal and central lobe in context of a GXT. The positive link of iAPF to temporal gamma power could be explained due to the fact that enhanced temporal gamma activity could be interpreted as provoked multisensory processing and attention (Jensen et al., 2007).

In the recovery process, solely temporal power of beta and gamma frequency as well as the total spectrum in EEG showed moderate negative correlations with HF power of HRV. EDR only revealed a small positive correlation to the temporal cortex supposing the respiratory rate to be less related to the temporal lobe at rest than during exercise. These aspects taken together, the relation of HF power to the temporal cortex can be interpreted as a prolonged negative relation of multisensory and sensorimotor integration, working memory and emotional processes to parasympathetic activity independent of the actual body activity carried out. During short- and middle-term recovery of an exhausting exercise, changes in the ANS are still expected due to a study determining effects one hour after a cycling exercise (Spring et al., 2018). Also, iAPF showed less moderate correlations in recovery than during the GXT. Only frontal theta correlated moderately negative with iAPF. This difference to GXT related correlations could rely on the relaxed recovery without the necessity of higher level of anticipatory attention and cortical excitability in task relevant brain areas (regarding alpha power reduction) as well as multisensory processing (regarding temporal gamma power increase). The inverse relation of theta power and iAPF could indicate a prolonged, more efficient use of attentional resources also during recovery after physical exhaustion. Summarizing, the hypothesis of a general negative relationship of EEG total spectrum power to HRV frequency parameters and a general positive relationship of EEG total spectrum power to RPE cannot be verified. According to different brain lobes and HRV relations between GXT conduction and rest, the hypothesized dependency of total brain activity on peripheral physiology should be reconsidered. It is recommended to differentiate between brain lobes regarding the particular provoked frontopolar and temporal lobe in this study. Although the correlation effects were just of a moderate to slightly strong level, they give sufficient information to foster a more precise investigation of the relationship between brain activity and HRV.

Overall, this study corroborates the existence of a (neuro-) physiological and subjectively perceived strain and their interaction evoked by pure, exhaustive physical activity. To our knowledge, the revealed effects in the temporal lobe may be declared as novel findings, as such that the temporal lobe also seems to play an important role in the interaction of brain activity and the cardiovascular system, next to the PFC. Furthermore, according to the original interpretation of Fisher’s statistics our results provide enough basis to be encouraged to conduct further similar studies. An analysis of brain lobe connectivity or a windowed Fourier analysis to compare time-dependent alterations in context with HRV presents further interesting research intentions. Based on the results of this study, we strongly recommend considering possible effects of pure physical activity in studies with an additional research question.