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For utilization of robotic assistive devices in everyday life, means fordetection and processing of erroneous robot actions are a focal aspect in thedevelopment of collaborative systems, especially when controlled via brainsignals. Though, the variety of possible scenarios and the diversity of usedrobotic systems pose a challenge for error decoding from recordings of brainsignals such as via EEG. For example, it is unclear whether humanoidappearances of robotic assistants have an influence on the performance. In thispaper, we designed a study in which two different robots executed the same taskboth in an erroneous and a correct manner. We find error-related EEG signals ofhuman observers indicating that the performance of the error decoding wasindependent of robot design. However, we can show that it was possible toidentify which robot performed the instructed task by means of the EEG signals.In this case, deep convolutional neural networks (deep ConvNets) could reachsignificantly higher accuracies than both regularized Linear DiscriminanatAnalysis (rLDA) and filter bank common spatial patterns (FB-CSP) combined withrLDA. Our findings indicate that decoding information about robot actionsuccess from the EEG, particularly when using deep neural networks, may be anapplicable approach for a broad range of robot designs.

The role of robot design in decoding error-related information from EEG signals of a human observer