In this study, we employed Morlet wavelet, sample entropy, and fractal dimension on EEG and EMG signal to extract the feature of physical fatigue in the exercise. The result may be helpful for rehabilitation in effectiveness evaluation. Twenty healthy subjects participated in cycling exercise, and their physiological signals, including EEG, EMG, and ECG were recorded. In addition, we recorded subjects' feeling of fatigue since each subject has different physical strength and tolerance of non-stopping exercise. Signals in different stages, namely, resting, early, middle and late stages of exercising, were analyzed. ECG signal was used to categorize subjects into two groups, namely, moderate fatigue and severe fatigue. In EEG results, the averaged power, sample entropy, and fractal dimension of signals indicated that resting stages before and after the exercise were distinct from exercising stage. In severe fatigue, the averaged power within each frequency band of EEG increased with the duration of exercise whereas the power ratio, denoted by (theta+ alpha)/beta, decreased gradually from the beginning of exercise until the resting after exercise. In addition, the EEG (C3) results of SE complexity ratio and FD complexity ratio decreased gradually from resting to last session of exercise in the moderate fatigue whereas in severe fatigue these ratios increased at the late exercising stage. Our results demonstrate that different patterns between moderate fatigue and severe fatigue can be effectively extracted by using the proposed methods.