Previous studies have demonstrated the possibility of using functional MRI to control a robot arm through a brain-machine interface by directly coupling haemodynamic activity in the sensory-motor cortex to the position of two axes. Here, we extend this work by implementing interaction at a more abstract level, whereby imagined actions deliver structured commands to a robot arm guided by a machine vision system. Rather than extracting signals from a small number of pre-selected regions, the proposed system adaptively determines at individual level how to map representative brain areas to the input nodes of a classifier network. In this initial study, a median action recognition accuracy of 90% was attained on five volunteers performing a game consisting of collecting randomly positioned coloured pawns and placing them into cups. The "pawn" and "cup" instructions were imparted through four mental imaginery tasks, linked to robot arm actions by a state machine. With the current implementation in MatLab language the median action recognition time was 24.3. s and the robot execution time was 17.7. s. We demonstrate the notion of combining haemodynamic brain-machine interfacing with computer vision to implement interaction at the level of high-level commands rather than individual movements, which may find application in future fMRI approaches relevant to brain-lesioned patients, and provide source code supporting further work on larger command sets and real-time processing.

Thoughts turned into high-level commands: Proof-of-concept study of a vision-guided robot arm driven by functional MRI (fMRI) signals.

Rosazza, Cristina Claudia;
2012-01-01

Abstract

Previous studies have demonstrated the possibility of using functional MRI to control a robot arm through a brain-machine interface by directly coupling haemodynamic activity in the sensory-motor cortex to the position of two axes. Here, we extend this work by implementing interaction at a more abstract level, whereby imagined actions deliver structured commands to a robot arm guided by a machine vision system. Rather than extracting signals from a small number of pre-selected regions, the proposed system adaptively determines at individual level how to map representative brain areas to the input nodes of a classifier network. In this initial study, a median action recognition accuracy of 90% was attained on five volunteers performing a game consisting of collecting randomly positioned coloured pawns and placing them into cups. The "pawn" and "cup" instructions were imparted through four mental imaginery tasks, linked to robot arm actions by a state machine. With the current implementation in MatLab language the median action recognition time was 24.3. s and the robot execution time was 17.7. s. We demonstrate the notion of combining haemodynamic brain-machine interfacing with computer vision to implement interaction at the level of high-level commands rather than individual movements, which may find application in future fMRI approaches relevant to brain-lesioned patients, and provide source code supporting further work on larger command sets and real-time processing.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11576/2671221
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