A neuro-based method for detecting context-dependent erroneous robot action

Abstract

Abstract-Validating appropriateness and naturalness of human-robot interaction (HRI) is commonly performed by taking subjective measures from human interaction partners, e.g. questionnaire ratings. Although these measures can be of high value for robot designers, they are very sensitive and can be inaccurate and/or biased. In this paper we propose and validate a neuro-based method for objectively validating robot behavior in HRI. We propose to detect from the electronencephalogram (EEG) of a human interaction partner, the perception of inappropriate / unexpected / erroneous robot behavior. To validate this method, we conducted an EEG experiment with a simplified HRI protocol in which a humanoid robot displayed context-dependent erroneous behavior from time to time. The EEG data taken from 13 participants revealed biologically plausible error-related potentials (ErrP) whose spatio-temporal distributions match well with related neuroscientific research. We further demonstrate that perceived erroneous robot action can reliably be modeled and detected from human EEG signals with classification accuracies on avg. 69.7±9.1%. These findings confirm principal feasibility of the proposed method and suggest that EEG-based ErrP detection can be used for quantitative evaluation and thus improvement of robot behavior.

Taking brain-computer-interfacing one step further: a portable, wireless system coupled with online linear discriminant analysis for the detection of error-related potentials

“…Previous literature reported the ErrP to be mainly represented within the theta band (4-8Hz) [9]. However, in some subjects the ErrP may leak into higher frequency spectra [5] and we consequently included the alfa band (8-13Hz) as well.…”

“…(3) as averages calculated over 100-200ms, 200-300ms, 150-250ms, 300-400ms, 400-500ms and 250-350ms, as previously described [5].…”

“…At first, ErrPs were employed to correct a subject's own actions [4], but were then quickly extrapolated to adjust the observed movement performed by a robot during the course of a binary task [5]. As is the case with other ERPs, the latency and amplitude of ErrPs depend on the subject's perception of the importance of the error, the degree of involvement of the subject in the task (the amplitude of the signal is lower if the subject follows an external device passively than if it controls this device) and on the frequency of the stimuli [4,6,7,8].…”

“…Taking advantage of scientific 64-electrode EEG arrays, prompt and valid recognition of correct/erratic robotic behaviour has been demonstrated with an accuracy of approximately 70% even during live recordings [5].…”

The Emotiv EPOC interface paradigm in Human-Computer Interaction

“…The idea is taken over in 2015 by Ehrlich and Cheng [15] which validate an objective method of correcting an error by detecting signals resulting from the perception of the inaccuracy / correctness of a bivalent task performed by a robot. Their result confirms previous reports and demonstrates that robot action can be detected and modelled from EEG signals with an accuracy of approximately 70%.…”