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Brain-actuated robotic systems have been proposed as a new control interface to translate different human intentions into appropriate motion commands for robotic applications. This study proposes a brain-actuated humanoid robot navigation system that uses an EEG-BCI. The experimental procedures consisted of offline training sessions, online feedback test sessions, and real-time control sessions. During the offline training sessions, amplitude features from the EEGs were extracted using band power analysis, and the informative feature components were selected using the Fisher ratio and the linear discriminant analysis (LDA) distance metric. The Intentional Activity Classifier (IAC) and the Motor Direction Classifier (MDC) were hierarchically structured and trained to build an asynchronous BCI system. During the navigation experiments, the subject controlled the humanoid robot in an indoor maze using the BCI system with real-time images from the camera on the robot's head. The results showed that three subjects successfully navigated the indoor maze using the proposed brain-actuated humanoid robot navigation system.