This paper describes a capstone design project by four undergraduate students (first four authors listed in alphabetical order by last name). A noninvasive brain computer interface (BCI) based on the steady state visual evoked potential (SSVEP) has been developed and utilized in controlling an iRobot platform remotely in real-time closed-loop fashion using video feedback from the robot's eye view to the operator over the internet. The operator selects commands by focusing gaze on one of four flickering checkerboards surrounding the video feedback window in order to navigate the robot as desired. The intended/desired control commands are sent to a laptop controlling the iRobot platform via remote wireless connection. Naive subjects are able to control and navigate the robot via the designed interface with minimal practice and classifier calibration. Typical command selection accuracy is over 95% within 4 seconds of the desired transition; most subjects are able to achieve such high accuracies with a 1 second delay.
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