EEG and EMG dataset for the detection of errors introduced by an active orthosis device

Abstract :

This paper presents a dataset containing recordings of the electroencephalogram (EEG) and the electromyogram (EMG) from eight subjects who were assisted in moving their right arm by an active orthosis device. The supported movements were elbow joint movements, i.e., flexion and extension of the right arm. While the orthosis was actively moving the subject’s arm, some errors were deliberately introduced for a short duration of time. During this time, the orthosis moved in the opposite direction. In this paper, we explain the experimental setup and present some behavioral analyses across all subjects. Additionally, we present an average event-related potential analysis for one subject to offer insights into the data quality and the EEG activity caused by the error introduction. The dataset described herein is openly accessible. The aim of this study was to provide a dataset to the research community, particularly for the development of new methods in the asynchronous detection of erroneous events from the EEG. We are especially interested in the tactile and haptic-mediated recognition of errors, which has not yet been sufficiently investigated in the literature. We hope that the detailed description of the orthosis and the experiment will enable its reproduction and facilitate a systematic investigation of the influencing factors in the detection of erroneous behavior of assistive systems by a large community.

Keywords :

EEG, event-related potential, EMG, orthosis, dataset, robotics, BCI, error detection

Files:

2023_EEG_and_EMG_dataset_for_the_detection_of_errors_in.pdf

Links:

https://arxiv.org/abs/2305.11996
https://arxiv.org/abs/2305.11996
https://www.frontiersin.org/articles/10.3389/fnhum.2024.1304311/full