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Updated: Feb 6, 2026

Haptic/Graphic Rehabilitation: Integrating a Robot into a Virtual Environment Library and Applying it to Stroke Therapy
Published on: August 8, 2011
Neural circuits activated by error amplification and haptic guidance training techniques during performance of a
Marie-Hélène Milot1, Laura Marchal-Crespo2,3, Louis-David Beaulieu4
1École de réadaptation, Faculté de médecine et des sciences de la santé, Université de Sherbrooke, Pavillon Gérald-Lasalle, 3001, 12e Avenue Nord, Sherbrooke, QC, J1H 5N4, Canada. marie-helene.milot@usherbrooke.ca.
Abstract:
To promote motor learning, robotic devices have been used to improve subjects' performance by guiding desired movements (haptic guidance-HG) or by artificially increasing movement errors to foster a more rapid learning (error amplification-EA). To better understand the neurophysiological basis of motor learning, a few studies have evaluated brain regions activated during EA/HG, but none has compared both approaches. The goal of this study was to investigate using fMRI which brain networks were activated during a single training session of HG/EA in healthy adults learning to play a computerized pinball-like timing task. Subjects had to trigger a robotic device by flexing their wrist at the correct timing to activate a virtual flipper and hit a falling ball towards randomly positioned targets. During training with HG/EA, subjects' timing errors were decreased/increased, respectively, by the robotic device to delay or accelerate their wrist movement. The results showed that at the beginning of the training period with HG/EA, an error-detection network, including cerebellum and angular gyrus, was activated, consistent with subjects recognizing discrepancies between their intended actions and the actual movement timing. At the end of the training period, an error-detection network was still present for EA, while a memory consolidation/automatization network (caudate head and parahippocampal gyrus) was activated for HG. The results indicate that training movement with various kinds of robotic input relies on different brain networks. Better understanding the neurophysiological underpinnings of brain processes during HG/EA could prove useful for optimizing rehabilitative movement training for people with different patterns of brain damage.
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