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To achieve reliable two-dimensional cursor control by noninvasive EEG-based brain-computer interface (BCI), users are typically required to receive long-term training to learn effective regulation of their brain rhythmic activities, and to maintain sustained attention during the operation. We proposed a two-dimensional BCI using event-related desynchronization and event-related synchronization associated with human natural behavior so that users no longer need long-term training or high mental loads to maintain concentration. In this study, we intended to investigate the performance of the proposed BCI associated with either physical movement or motor imagery with an online center-out cursor control paradigm. Genetic algorithm (GA)-based mahalanobis linear distance (MLD) classifier and decision tree classifier (DTC) were used in feature selection and classification and a model adaptation method was employed for better decoding of human movement intention from EEG activity. The results demonstrated effective control accuracy for this four-class classification: as high as 77.1% during online control with physical movement and 57.3% with motor imagery. This suggests that based on this preliminary testing, two-dimensional BCI control can be achieved without long-term training.