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Updated: Jun 18, 2026

Motor Imagery Performance Through Embodied Digital Twins in a Virtual Reality-Enabled Brain-Computer Interface Environment
Published on: May 10, 2024
Motor imagery supported by augmented reality activates motor brain areas similarly to physical movements
Elena Fenoglio1,2, Florencia Garro1, Anna Bucchieri1
1Rehab Technologies Lab, Italian Institute of Technology, Genoa, Italy.
Abstract:
Objective. Motor imagery (MI) is a well-established cognitive process that may enhance motor skills and recovery during motor rehabilitation. Integrating action observation (AO) and augmented reality (AR) into MI tasks is a promising approach to enhance brain modulation and improve motor learning, yet it remains under-explored. This study examines the impact of AR on brain modulation during tasks combining MI and AO, and its implications for motor learning assessed through an implicit sequence learning paradigm.Approach. 35 participants were separated into two groups: a motor execution (ME) group performing a physical reaching task, and a MI group performing the same task within an augmented-reality-based kinesthetic MI paradigm. Event-related synchronization/desynchronization was analyzed in source-localized electroencephalography data between and within groups.Main results. We found similar brain modulation patterns between ME and MI groups specifically when MI was supported by AR, particularly in alpha and beta bands during movement planning and execution phases. Moreover, we observed high inter-individual variability: a subgroup of MI participants did not produce the expected neural response, showing reduced modulation in motor-related regions compared to those who responded as expected. Furthermore, reaction times were compared during physical movements, through implicit repeated and random sequences of reaching movements. We observed non-significant trends towards faster responses in implicit sequences, which may suggest potential implicit motor learning.Significance. These exploratory results offer useful insights into augmented-reality-supported MI, particularly for users who may require personalized approaches to benefit from MI paradigms, as in brain-computer interface applications.

