The importance of robotic assistive devices grows in our work and everyday
life. Cooperative scenarios involving both robots and humans require safe
human-robot interaction. One important aspect here is the management of robot
errors, including fast and accurate online robot-error detection and
correction. Analysis of brain signals from a human interacting with a robot may
help identifying robot errors, but accuracies of such analyses have still
substantial space for improvement. In this paper we evaluate whether a novel
framework based on deep convolutional neural networks (deep ConvNets) could
improve the accuracy of decoding robot errors from the EEG of a human observer,
both during an object grasping and a pouring task. We show that deep ConvNets
reached significantly higher accuracies than both regularized Linear
Discriminant Analysis (rLDA) and filter bank common spatial patterns (FB-CSP)
combined with rLDA, both widely used EEG classifiers. Deep ConvNets reached
mean accuracies of 75% +/- 9 %, rLDA 65% +/- 10% and FB-CSP + rLDA 63% +/- 6%
for decoding of erroneous vs. correct trials. Visualization of the time-domain
EEG features learned by the ConvNets to decode errors revealed spatiotemporal
patterns that reflected differences between the two experimental paradigms.
Across subjects, ConvNet decoding accuracies were significantly correlated with
those obtained with rLDA, but not CSP, indicating that in the present context
ConvNets behaved more 'rLDA-like' (but consistently better), while in a
previous decoding study with another task but the same ConvNet architecture, it
was found to behave more 'CSP-like'. Our findings thus provide further support
for the assumption that deep ConvNets are a versatile addition to the existing
toolbox of EEG decoding techniques, and we discuss steps how ConvNet EEG
decoding performance could be further optimized
