Event ANT Neuromeeting 2025 - Berlin starts on Jan 16, 2025, 9:00:00 AM (Europe/Berlin)
EEG activity elicited by motor learning and to predict workload under microgravity
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1/17/25, 2:00 PM - 1/17/25, 2:30 PM (Europe/Berlin) (30 minutes)
EEG activity elicited by motor learning and to predict workload under microgravity
Prof. Dr. Elsa Kirchner
Faculty of Engineering Department of Electrical Engineering and Information Technology, Head of Department Medical Technology Systems at Universität Duisburg-Essen
https://www.uni-due.de/smt/kirchner.php
Faculty of Engineering Department of Electrical Engineering and Information Technology, Head of Department Medical Technology Systems at Universität Duisburg-Essen
https://www.uni-due.de/smt/kirchner.php

Elsa A. Kirchner studied Biology and began her research career in 1999 at the Department of Brain and Cognitive Sciences at MIT in Boston, USA. Subsequently she became head of the Brain & Behavioral Lab in the Robotics Group at the University of Bremen, Germany. In parallel, she worked for the German Research Center for Artificial Intelligence (DFKI). There she led teams in human-machine interaction and interactive machine learning. Since 2021, she has been a professor for " Medical Technology Systems " at the University of Duisburg-Essen, Germany, and continues to cooperate with the DFKI. Since 2022, she has been co-chair of Working Group 7 "Learning Robotic Systems" of Germanys PlaXorm “Lernende Systeme” and a founding member of the DLR network Space2Health. In 2023, she was appointed as a member of the Council for Technological Sovereignty of the BMBF. Her research interests include human-robot interaction, focusing on the analysis of multimodal biosignals recorded from humans. Further she applies ML methods to use biosignals to enable or improve human-robot interaction. She also conducts research on robot learning from humans and on embedded and embodied AI.

Fine motor movements must be adapted or relearned when relevant bodily or environmental conditions such as gravity change. Exoskeletons may offer a new way to train astronauts' fine motor skills for microgravity environments. In a parabolic flight, we investigated whether fine motor skills trained with an exoskeleton that simulates microgravity are transferable to conditions with real microgravity. For space travel, however, it is not only important to train astronauts appropriately, but also to monitor physical and mental exertion. We show that changes in workload can be detected by EEG recorded with dry electrodes and how fine motor learning and unaccustomed physical and mental effort change EEG activity.