Publikation
Asynchronous classification of error-related potentials in human-robot interaction
Su-Kyoung Kim; Michael Maurus; Mathias Trampler; Marc Tabie; Elsa Andrea Kirchner
In: 25th International Conference on Human-Computer Interaction. International Conference on Human-Computer Interaction (HCII-2023), July 23-28, Copenhagen, Denmark, Springer, 7/2023.
Zusammenfassung
The use of implicit evaluations of humans such as electroencephalogram
(EEG)-based human feedback is relevant for robot applications,
e.g., robot learning or corrections of robot's actions. In the presented
study, we implemented a scenario, in which a simulated robot
communicates with its human partner through speech and gestures. The
robot announces its intention verbally and selects the appropriate action
using pointing gestures. The human partner in turn implicitly evaluates
whether the robot's verbal announcement matches the robot's action
choice. Error-related potentials (ErrPs) are expressions of this implicit
evaluation, which are triggered in case of discrepancies between
the robot's verbal announcement and the corresponding actions (pointing
gestures) chosen by the robot. In our scenario, the task takes a long
time. Therefore, asynchronous EEG classifications that continuously segment
EEGs are advantageous or even necessary. However, asynchronous
EEG classifications are challenging due to the large number of false positives
during the long task time of the robot. In this work, we propose an
approach to improve asynchronous classification performance by selecting
and extracting features that are only relevant for EEG classifications
in the long time series that the robot needs to perform tasks.We achieved
a high classification performance, i.e., a mean balanced accuracy of 91%
across all subjects. However, we also found some di erences between subjects
in classification performance. In future work, it is useful to extend
the proposed approach of forward and backward sliding windows and
their combinations with individual feature selection adaptation to avoid
the variability of classification performance between subjects.
Projekte
TransFit - Flexible Interaktion für Infrastrukturaufbau mittels Teleoperation und direkte Kollaboration und Transfer in Industrie 4.0,
M-RoCK - Modellierung der Mensch-Maschine-Interaktion zur kontinuierlichen Verbesserung des Roboterverhaltens
M-RoCK - Modellierung der Mensch-Maschine-Interaktion zur kontinuierlichen Verbesserung des Roboterverhaltens