Publication
Classification of error-related potentials evoked during observation of human motion sequences
Su-Kyoung Kim; Julian Liersch; 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.
Abstract
In recent studies, electroencephalogram (EEG)-based interfaces
that enable to infer human intentions and to detect implicit human
evaluation contributed to the development of effective adaptive human-machine
interfaces. In this paper, we propose an approach to allow systems
to adapt based on implicit human evaluation which can be extracted
by using EEGs. In our study, human motion segments are evaluated according
to an EEG-based interface. The goal of the presented study is
to recognize incorrect motion segments before the motion sequence is
completed. This is relevant for early system adaptation or correction.
To this end, we recorded EEG data of 10 subjects while they observed
human motion sequences. Error-related potentials (ErrPs) are used to
recognize observed erroneous human motion. We trained an EEG classifer
(i.e., ErrP decoder) that detects erroneous motion segments as part
of motion sequences. We achieved a high classification performance, i.e.,
a mean balanced accuracy of 91% across all subjects. The results show
that it is feasible to distinguish between correct and incorrect human
motion sequences based on the current intentions of an observer. Further,
it is feasible to detect incorrect motion segments in human motion
sequences by using ErrPs (i.e., implicit human evaluations) before a motion
sequence is completed. This is possible in real time and especially
before human motion sequences are completed. Therefore, our results are
relevant for human-robot interaction tasks, e.g., in which model adaptation
of motion prediction is necessary before the motion sequence is
completed
Projects
- HaLeR - HaLeR - Detection of action deviations through learning with limited computing resources
- M-RoCK - Human-Machine Interaction Modeling for Continuous Improvement of Robot Behavior