Few studies have investigated changes in brain activity during exercise. It is still unclear how exercise affects the brain cortical activity. The aim of this study is to investigate different physiological features during a sustained cycling exercise. We recorded Electrocardiography (ECG) and Electroencephalography (EEG) from participants before, during, and after exercise. We investigate the correlation between heart rate and other physiological parameters such as EEG spectral power and EEG complexity. Forty-four healthy subjects participated in this study. The EEG was recorded from 4 scalp sites, which were C1, C2, P1, and P2. Subjects were asked to ride the bicycle continuously for nine minutes. Signals of EEG and ECG in resting condition were recorded for four minutes before exercise and another four minutes after exercise. In addition, we recorded subjects' rating of perceived exertion (RPE) since each subject has different physical strength and tolerance of non-stopping exercise. The average-To-maximal heart rate ratio (AMHRR) was calculated for each subject, which will be used to explore the relationship between exercise workload and physiological parameters. Moreover, we employed Morlet wavelet and Higuchi's fractal dimension analysis methods on EEG signals. Finally, we use regression analysis to examine the relationship between the various physiological parameters and AMHRR. Results indicate that RPE had a linearly correlation with AMHRR, which allows us to investigate the changes of EEG objectively from AMHRR. We found that the EEG normalized power had a positive correlation with heart rate or with exercise workload. The highest increased in EEG normalized power occurred when the participants were in their highest heart rate. On the other hand, we observed the EEG Higuchi's fractal dimension (HFD) had a negative correlation with heart rate. It may infer that a reduction of EEG complexity during exercise may cause by the increase of neuronal synchrony. The same phenomenon, an increase in EEG power at a specific frequency, is typically observed due to a synchronized firing of neurons. In summary, these results demonstrated that the EEG can be used as a salient tool to differentiate different exercise workload.