  For utilization of robotic assistive devices in everyday life, means for
detection and processing of erroneous robot actions are a focal aspect in the
development of collaborative systems, especially when controlled via brain
signals. Though, the variety of possible scenarios and the diversity of used
robotic systems pose a challenge for error decoding from recordings of brain
signals such as via EEG. For example, it is unclear whether humanoid
appearances of robotic assistants have an influence on the performance. In this
paper, we designed a study in which two different robots executed the same task
both in an erroneous and a correct manner. We find error-related EEG signals of
human observers indicating that the performance of the error decoding was
independent of robot design. However, we can show that it was possible to
identify which robot performed the instructed task by means of the EEG signals.
In this case, deep convolutional neural networks (deep ConvNets) could reach
significantly higher accuracies than both regularized Linear Discriminanat
Analysis (rLDA) and filter bank common spatial patterns (FB-CSP) combined with
rLDA. Our findings indicate that decoding information about robot action
success from the EEG, particularly when using deep neural networks, may be an
applicable approach for a broad range of robot designs.
