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

TL;DR

This study investigates how robot design influences the ability to decode error-related EEG signals from human observers, finding that deep neural networks can effectively identify robot actions regardless of appearance, supporting broader application in assistive robotics.

Contribution

The paper demonstrates that deep convolutional neural networks outperform traditional methods in decoding robot action errors from EEG signals, independent of robot appearance.

Findings

Error-related EEG signals are detectable regardless of robot design.

Deep ConvNets achieve higher accuracy than traditional methods.

Robot appearance does not significantly affect error decoding performance.

Abstract

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.