Motion sickness (MS) is a normal response to real, perceived, or even anticipated movement. People tend to get motion sickness on a moving boat, train, airplane, car, or amusement park rides. Although many motion sickness-related biomarkers have been identified, but how to estimate human's motion sickness level (MSL) is a big challenge in the operational environment. Traditionally, questionnaire and physical check are the common ways to passively evaluate subject's sickness level. Our past studies had investigated the EEG activities correlated with motion sickness in a virtual-reality based driving simulator. The driving simulator comprised an actual automobile mounted on a Stewart motion platform with six degrees of freedom, providing both visual and vestibular stimulations to induce motion-sickness in a manner that is close to that in daily life. EEG data were acquired at a sampling rate of 500 Hz using a 32-channel EEG system. The acquired EEG signals were analyzed using independent component analysis (ICA) and time-frequency analysis to assess EEG correlates of motion sickness. Subject's degree of motion-sickness was simultaneously and continuously reported using an onsite joystick, providing non-stop psychophysical references to the recorded EEG changes. We found that the parietal, motor, occipital brain regions exhibited significant EEG power changes in response to vestibular and visual stimuli. Based on these findings and experimental results, this study aims to develop an EEG-based system to estimate subject's motion sickness level upon the EEG power spectra from motion-sickness related brain areas. The MS evaluation system can be applied to early detection of the subject's motion sickness and prevent its uncomfortable syndromes in our daily life. Furthermore, the experiment results could also lead to a practical human-machine interface for noninvasive monitoring of motion sickness of drivers or passengers in real-world environments.