In some cases, temporary or permanent occlusion of the arteries that participate in providing cerebral blood flow is needed during surgery. It is important to monitor the brain condition to prevent from a sequela. EEG (electroencephalogram) analysis is one of the most familiar methods. The goal of this study is to develop integrated EEG analysis software. The software consists of data acquisition, waveform memory management, and data analysis components. The advantages of this system are real-time analysis, generality, expandability and portability. At this point in time, it can display waveform, spectrum and topography changes of each frequency component for any electrode group, and DRT (deviation ratio topography) in real time. Moreover, the system warns the operator via alarm when the EEG changes significantly. This function makes it possible to reduce the operator's task, because the operator is able to understand the patient's condition without directly observing the monitoring system. In this study, we also propose the application of moving average-type neural networks (MANN) to analyze the EEG. In this method, the measured EEG is divided into overlapped period of 30 sec. A MANN is trained to predict the EEG using five previous samples in each period. After training, the connecting weights of each MANN are compared using the inner product to evaluate changes in brain condition. According to the results of clinical data analysis, the usefulness of both the MANN analysis and DRT analysis was confirmed.