The role of robot design in decoding error-related information from EEG signals of a human observer
This research addresses the challenge of detecting erroneous robot actions in brain-controlled assistive devices, showing that deep learning can enhance decoding across diverse robot designs, though it is incremental as it builds on existing EEG error decoding methods.
The study investigated whether robot design affects error decoding from EEG signals of human observers, finding that error decoding performance was independent of robot design, but deep convolutional neural networks achieved significantly higher accuracies (up to 85%) in identifying which robot performed a task compared to traditional methods.
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.