Biometric personal authentication is an emerging technology in information security. One resulting problem has been a recent increase in fraud based on falsified biometric data concerning biological information. Brainwave-based identification is a promising biometric tool to prevent impostor attacks. Many researchers have reported biometric results using electroencephalogram (EEG) activity. The brainwave features of each individual are unique and have the potential for use in biometric authentication. Security can be enhanced by employing as many EEG features of an individual as possible. Although it takes time to measure brain waves at present, this authentication method can potentially be used for special security areas in the future. There are several approaches to brainwave biometrics using cognitive processes. We investigated the motor imagery for movements of the left hand, right hand, tongue, and both feet for brainwave biometrics. The nature of brain signal analysis parallels voice signal analysis in some respects. Hence, we applied the cepstral analysis method, which is commonly used in speech recognition, for feature extraction for brainwave biometrics. In our results, we identified almost all nine of the subjects correctly. We tested the performance of our biometric system using the Mahalanobis distance as the threshold and estimated the equal error rate (EER) value to be 0.17.