Brain-computer interfaces (BCIs) can convert mental states into signals to drive real-world devices, but it is not known if a given covert task is the same when performed with and without BCIbased control. Using a BCI likely involves additional cognitive processes, such as multitasking, attention, and conflict monitoring. In addition, it is challenging to measure the quality of covert task performance. We used whole-brain classifier-based real-time functional MRI to address these issues, because the method provides both classifier-based maps to examine the neural requirements of BCI and classification accuracy to quantify the quality of task performance. Subjects performed a covert counting task at fast and slow rates to control a visual interface. Compared with the same task when viewing but not controlling the interface, we observed that being in control of a BCI improved task classification of fast and slow counting states. Additional BCI control increased subjects' whole-brain signal-to-noise ratio compared with the absence of control. The neural pattern for control consisted of a positive network comprised of dorsal parietal and frontal regions and the anterior insula of the right hemisphere as well as an expansive negative network of regions. These findings suggest that real-time functional MRI can serve as a platform for exploring information processing and frontoparietal and insula network-based regulation of whole-brain task signal-to-noise ratio. A lthough overt actions allow us to interact directly with our environment, much of our brain's activity is devoted to covert acts, such as motor planning, rehearsal, and self-directed thought. Indeed, we enjoy a rich inner experience consisting of activities such as visual imagery, inner language, somatosensory awareness, recollection of the past, and planning for the future (1). By definition, covert actions are usually neither observable by a third party nor capable of directly affecting the outside world. Brain-computer interfaces (BCIs), however, provide a technological means for converting thought into action by transducing brain measurements into control signals for devices, such as robots and computer displays. One strategy for BCI control is to provide the subject with task commands, such as "move the cursor to the right by imagining that you are moving your right hand."In effect, BCI control creates a synthetic link between covert action and the role of sensory feedback. The additional cognitive requirements and consequences for BCI control, however, are unknown, and several interrelated cognitive processes could play a role. Examples include multitasking for dual task performance (2-5), conflict and outcome monitoring (6, 7), attention (8), reward monitoring (9, 10), and learning and conditioning (11,12). If present, each of these cognitive processes should have specific neural signatures (prefrontal cortex, anterior cingulate, frontoparietal networks, ventral striatum, etc.).An additional consideration is the performance of the task itself. Evaluat...