On the neural correlates of motor imagery with an extra virtual arm

Dataset Overview This dataset contains electroencephalographic (EEG) recordings from participants performing motor imagery (MI) for biological limbs (left and right arms) and a novel extra virtual arm (XVA). The data is structured to compare neural patterns across these limb types and evaluate the impact of tactile feedback on learning a new effector. Subject Distribution The dataset comprises on the EEG of 28 subjects, split between two experimental groups: Tactile Group (n=13): Participants who received physical pressure feedback on their chest corresponding to the XVA's movement during the conditioning phase. Non-Tactile Group (n=15): A control group that performed the same tasks without physical feedback. Demographics: All subjects are healthy, right-handed individuals with a mean age of approximately 23.9 years. Experimental Phases & Baseline The data is organized into three distinct blocks of trials, plus a dedicated baseline: Baseline (Eyes Open): Recorded at the beginning of the protocol to assess resting neural activity while the eyes are open. PRE (Pre-conditioning): Initial MI trials where participants watched videos of the movements to guide their imagery for the left arm, right arm, and XVA. PO (Conditioning Phase): An immersion phase in Virtual Reality (VR) where participants observed an avatar's movements from a first-person perspective. For the Tactile group, this included haptic feedback. POST (Post-conditioning): A final set of MI trials to evaluate changes in neural signatures and decoding accuracy following the VR training. The EEG data files have all 54 channels of interest used in the research, with the pre-processing steps described at DOI 10.1109/TMRB.2025.3625073. For the experimental tasks the files contain a data struct with the EEG from each arm (channels x time x trials), time vector and sample frequency of the recording. The baseline files was also epoched in the samel trial manner. Fundings This work was supported by the Bertarelli Foundation; the Swiss National Science Foundation through the National Centre of Competence in Research (NCCR) Robotics Grassroots Project; and the European Union’s Horizon 2020 research and innovation program under Marie SklodowskaCurie grant 754354. This work was also supported by the Horizon Europe Research & Innovation Program under grant 101092612 (Social and hUman ceNtered XR—SUN project) the #NEXTGENERATIONEU (NGEU) and partially funded by the Ministry of University and Research (MUR), National Recovery and Resilience Plan (NRRP) with two projects: MNESYS (PE0000006)—A Multiscale integrated approach to the study of the nervous system in health and disease (DN. 1553 11.10.2022) and THE (IECS00000017)—Tuscany Health Ecosystem (DN. 1553 11.10.2022). Open access funding provided by Ecole Polytechnique Federale de Lausanne.