On the Neural Correlates of Motor Imagery With an Extra Virtual Arm

Motor augmentation (MA) is an emerging field at the intersection of engineering, robotics, and neuroscience, aiming to enhance human capabilities through the integration of extra limbs. This concept leverages the body’s physiological redundancies, including those within the nervous system. This study examined motor imagery (MI) involving a virtual extra arm, focusing on differentiating its neural patterns from those of biological limbs. Thirty participants performed unimanual reaching MI tasks before (Pre) and after (Post) a conditioning phase in a virtual environment, during which half of the participants received tactile feedback on the movement of the extra arm. Electroencephalographic (EEG) recordings revealed distinct event-related desynchronization (ERD) in α and β rhythms between the extra and biological limbs. Additionally, a Riemannian decoder successfully classified MI for the left, right, and extra virtual arm, providing further evidence of distinct neural patterns. While the conditioning played a role in the ERD’s neural signatures, we did not find the same effects on the decoding. We believe that more complex movements, other sensory encoding modalities, or longer conditioning periods would likely strengthen the connection between tactile feedback and neural control.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Medical Robotics and Bionics
Published
2025-10-23
DOI
https://doi.org/10.1109/tmrb.2025.3625073
Primary Topic
Action Observation and Synchronization
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

On the Neural Correlates of Motor Imagery With an Extra Virtual Arm

Solaiman Shokur, Leonardo Pollina, Karin A. Buetler, Laura Marchal–Crespo et al.
IEEE Transactions on Medical Robotics and Bionics
Action Observation and Synchronization
article

On the Neural Correlates of Motor Imagery With an Extra Virtual Arm

Solaiman Shokur, Leonardo Pollina, Karin A. Buetler, Laura Marchal–Crespo, Silvestro Micera, Daniel J. L. L. Pinheiro
article en

Abstract

Motor augmentation (MA) is an emerging field at the intersection of engineering, robotics, and neuroscience, aiming to enhance human capabilities through the integration of extra limbs. This concept leverages the body’s physiological redundancies, including those within the nervous system. This study examined motor imagery (MI) involving a virtual extra arm, focusing on differentiating its neural patterns from those of biological limbs. Thirty participants performed unimanual reaching MI tasks before (Pre) and after (Post) a conditioning phase in a virtual environment, during which half of the participants received tactile feedback on the movement of the extra arm. Electroencephalographic (EEG) recordings revealed distinct event-related desynchronization (ERD) in α and β rhythms between the extra and biological limbs. Additionally, a Riemannian decoder successfully classified MI for the left, right, and extra virtual arm, providing further evidence of distinct neural patterns. While the conditioning played a role in the ERD’s neural signatures, we did not find the same effects on the decoding. We believe that more complex movements, other sensory encoding modalities, or longer conditioning periods would likely strengthen the connection between tactile feedback and neural control.

IEEE Transactions on Medical Robotics and BionicsVol. 7(4)
University of Bern (CH), Scuola Superiore Sant'Anna (IT), Erasmus MC (NL), École Polytechnique Fédérale de Lausanne (CH)
National Science Foundation, University of Bern, Fondation Bertarelli, European Commission, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Technische Universiteit Delft, Erasmus Medisch Centrum, Université de Lausanne, Scuola Superiore Sant'Anna, HORIZON EUROPE Framework Programme
Openalex Percentile: Top 65%
Action Observation and Synchronization
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.