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The detection of event related potentials and their usage for innovative tasks became a mature research topic in the last couple of years for brain computer interfaces. However, the typical experimental setups are usually highly controlled and designed to actively evoke specific brain activity like the P300 event related potential. In this paper, we show that the detection and passive usage of the P300 related brain activity is possible in highly uncontrolled and noisy application scenarios where the subjects are performing demanding senso-motor task, i.e., telemanipulation of a real robotic arm. In the application scenario, the subject wears an exoskeleton to control a robotic arm, which is presented to him in a virtual scenario. While performing the telemanipulation task he has to respond to important messages. By online analysis of the subject’s electroencephalogram we detect P300 related target recognition processes to infer on upcoming response behavior or missing of response behavior in case a target was not recognized. We show that a classifier that is trained to distinguish between brain activity evoked by recognized task relevant stimuli and ignored frequent task irrelevant stimuli can be applied to classify between brain activity evoked by recognized task relevant stimuli and brain activity that is evoked in case that task relevant stimuli are not recognized.

Online Detection of P300 related Target Recognition Processes During a Demanding Teleoperation Task - Classifier Transfer for the Detection of Missed Targets