Recent developments in brain-machine interfaces (BMIs) have proposed the use of error-related potentials as a type of cognitive information that can provide a reward or feedback to adapt the BMI during operation, either to directly control devices or to teach a robot how to solve a task. Due to the nature of these signals, all the proposed error-based BMIs work under the assumption that the response is time-locked to the known onset of the event. However, during the continuous operation of a robot, there may not exist a clear event that elicits the error potential. Indeed, it is not clear whether such a potential will appear and whether it can be detected online. Furthermore, calibrating such a system is not trivial due to the unknown instant at which the user detects the error. This paper presents a first study towards the detection of error potentials from EEG measurements during continuous trajectories performed by a virtual device. We present a experimental protocol that allows us to train the decoder and detect the errors in single trial. Further analyses show that the brain activity used by the decoder comes from brain areas involved in error processing.
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