Haptic fMRI: A novel five DOF haptic interface for multi-axis motor neuroscience experiments

We present a novel electromagnetically actuated Haptic fMRI Interface with five degrees-of-freedom (DOF), HFI-5. The interface uses two three DOF devices connected with a gimbal in a closed kinematic chain to achieve three-axis translation and two-axis rotation. To highlight the device's design, we develop a taxonomy of similar devices and demonstrate why HFI-5's design excels. We use a cross-correlation based novel analysis method to demonstrate that HFI-5 is transparent by showing that it does not affect unconstrained subject motions. This is due to its low friction (< 0.31 N) and lightweight design. Next, we show that HFI-5 can accurately track positions, velocities, and accelerations, in part due to a 7.5 kHz servo loop. Finally, we demonstrate that HFI-5 is fMRI-compatible and does not interfere with fMRI scans even while applying large torques with its electromagnetic motors.

[1]  Dejan B. Popovic,et al.  Influence of planar manipulandum to the hand trajectory during point to point movement , 2011, 2011 IEEE International Conference on Rehabilitation Robotics.

[2]  Oussama Khatib,et al.  Haptic fMRI: Combining functional neuroimaging with haptics for studying the brain's motor control representation , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[3]  Oussama Khatib,et al.  Using Haptic fMRI to Enable Interactive Motor Neuroimaging Experiments , 2014, ISER.

[4]  Fabrizio Sergi,et al.  Kinesthetic Feedback During 2DOF Wrist Movements via a Novel MR-Compatible Robot , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[5]  J. Krakauer,et al.  The interaction between training and plasticity in the poststroke brain. , 2013, Current opinion in neurology.

[6]  Oussama Khatib,et al.  A New Actuation Approach for Haptic Interface Design , 2009, Int. J. Robotics Res..

[7]  Jean-Sébastien Plante,et al.  Manipulation in MRI devices using electrostrictive polymer actuators: with an application to reconfigurable imaging coils , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[8]  Kevin Murphy,et al.  How long to scan? The relationship between fMRI temporal signal to noise ratio and necessary scan duration , 2007, NeuroImage.

[9]  Oussama Khatib,et al.  A New Actuation Approach for Haptic Interface Design , 2009, Int. J. Robotics Res..

[10]  J. Krakauer Motor learning: its relevance to stroke recovery and neurorehabilitation. , 2006, Current opinion in neurology.

[11]  Yasmin L. Hashambhoy,et al.  Neural Correlates of Reach Errors , 2005, The Journal of Neuroscience.

[12]  John Kenneth Salisbury,et al.  Haptic Rendering: Introductory Concepts , 2004, IEEE Computer Graphics and Applications.

[13]  Kay M. Stanney,et al.  Deriving haptic design guidelines from human physiological, psychophysical, and neurological foundations , 2004, IEEE Computer Graphics and Applications.

[14]  Oussama Khatib,et al.  Haptic fMRI: Accurately estimating neural responses in motor, pre-motor, and somatosensory cortex during complex motor tasks , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[15]  J. Krakauer,et al.  Error correction, sensory prediction, and adaptation in motor control. , 2010, Annual review of neuroscience.

[16]  Etienne Burdet,et al.  fMRI Compatible Haptic Interfaces to Investigate Human Motor Control , 2006, ISER.

[17]  K. Sunnerhagen,et al.  Assessment and Training in a 3-Dimensional Virtual Environment With Haptics: A Report on 5 Cases of Motor Rehabilitation in the Chronic Stage After Stroke , 2007, Neurorehabilitation and neural repair.

[18]  Marko Munih,et al.  Phantom haptic device upgrade for use in fMRI , 2009, Medical & Biological Engineering & Computing.

[19]  Oussama Khatib,et al.  Haptic fMRI: Using classification to quantify task-correlated noise during goal-directed reaching motions , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[20]  R. Gassert,et al.  Investigation of a Cable Transmission for the Actuation of MR Compatible Haptic Interfaces , 2006, The First IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, 2006. BioRob 2006..

[21]  S. Kastner,et al.  Complex organization of human primary motor cortex: a high-resolution fMRI study. , 2008, Journal of neurophysiology.

[22]  Marcia Kilchenman O'Malley,et al.  Design and validation of the RiceWrist-S exoskeleton for robotic rehabilitation after incomplete spinal cord injury , 2014, Robotica.

[23]  Martin Buss,et al.  Development of a 3 DoF MR-Compatible Haptic Interface for Pointing and Reaching Movements , 2010, EuroHaptics.

[24]  Oussama Khatib,et al.  Spanning large workspaces using small haptic devices , 2005, First Joint Eurohaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems. World Haptics Conference.

[25]  Oussama Khatib,et al.  Mapping stiffness perception in the brain with an fMRI-compatible particle-jamming haptic interface , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[26]  Heidi Sveistrup,et al.  Motor rehabilitation using virtual reality , 2004, Journal of NeuroEngineering and Rehabilitation.

[27]  N. Logothetis What we can do and what we cannot do with fMRI , 2008, Nature.

[28]  Thomas H. Massie,et al.  The PHANToM Haptic Interface: A Device for Probing Virtual Objects , 1994 .