Proprioceptive Feedback and Brain Computer Interface (BCI) Based Neuroprostheses

Brain computer interface (BCI) technology has been proposed for motor neurorehabilitation, motor replacement and assistive technologies. It is an open question whether proprioceptive feedback affects the regulation of brain oscillations and therefore BCI control. We developed a BCI coupled on-line with a robotic hand exoskeleton for flexing and extending the fingers. 24 healthy participants performed five different tasks of closing and opening the hand: (1) motor imagery of the hand movement without any overt movement and without feedback, (2) motor imagery with movement as online feedback (participants see and feel their hand, with the exoskeleton moving according to their brain signals, (3) passive (the orthosis passively opens and closes the hand without imagery) and (4) active (overt) movement of the hand and rest. Performance was defined as the difference in power of the sensorimotor rhythm during motor task and rest and calculated offline for different tasks. Participants were divided in three groups depending on the feedback receiving during task 2 (the other tasks were the same for all participants). Group 1 (n = 9) received contingent positive feedback (participants' sensorimotor rhythm (SMR) desynchronization was directly linked to hand orthosis movements), group 2 (n = 8) contingent “negative” feedback (participants' sensorimotor rhythm synchronization was directly linked to hand orthosis movements) and group 3 (n = 7) sham feedback (no link between brain oscillations and orthosis movements). We observed that proprioceptive feedback (feeling and seeing hand movements) improved BCI performance significantly. Furthermore, in the contingent positive group only a significant motor learning effect was observed enhancing SMR desynchronization during motor imagery without feedback in time. Furthermore, we observed a significantly stronger SMR desynchronization in the contingent positive group compared to the other groups during active and passive movements. To summarize, we demonstrated that the use of contingent positive proprioceptive feedback BCI enhanced SMR desynchronization during motor tasks.

[1]  H. Robinson.  Principles and Procedures of Statistics , 1961 .

[2]  Ingrid Daubechies,et al.  Where do wavelets come from? A personal point of view , 1996, Proc. IEEE.

[3]  Sachiko Koyama,et al.  Identification of motor and sensory brain activities during unilateral finger movement: spatiotemporal source analysis of movement-associated magnetic fields , 1997, Experimental Brain Research.

[4]  Toshio Tsuji,et al.  EMG-based human-robot interface for rehabilitation aid , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[5]  N. Hogan,et al.  A novel approach to stroke rehabilitation , 2000, Neurology.

[6]  Piet M. T. Broersen,et al.  Autoregressive spectral estimation by application of the Burg algorithm to irregularly sampled data , 2002, IEEE Trans. Instrum. Meas..

[7]  David M. Santucci,et al.  Learning to Control a Brain–Machine Interface for Reaching and Grasping by Primates , 2003, PLoS biology.

[8]  H. Flor,et al.  A multimodal brain-based feedback and communication system , 2004, Experimental Brain Research.

[9]  C. Braun,et al.  Motor learning elicited by voluntary drive. , 2003, Brain : a journal of neurology.

[10]  A. Labarga,et al.  Alpha and beta oscillatory activity during a sequence of two movements , 2004, Clinical Neurophysiology.

[11]  N. Hogan,et al.  Customized interactive robotic treatment for stroke: EMG-triggered therapy , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[12]  Christa Neuper,et al.  Walking by Thinking: The Brainwaves Are Crucial, Not the Muscles! , 2006, PRESENCE: Teleoperators and Virtual Environments.

[13]  A. Abbott.  Neuroprosthetics: In search of the sixth sense , 2006, Nature.

[14]  N. Hogan,et al.  Motions or muscles? Some behavioral factors underlying robotic assistance of motor recovery. , 2006, Journal of rehabilitation research and development.

[15]  Andrew B. Schwartz,et al.  Brain-Controlled Interfaces: Movement Restoration with Neural Prosthetics , 2006, Neuron.

[16]  Christa Neuper,et al.  Future prospects of ERD/ERS in the context of brain-computer interface (BCI) developments. , 2006, Progress in brain research.

[17]  N. Thakor,et al.  Journal of Neuroengineering and Rehabilitation Open Access a Brain-computer Interface with Vibrotactile Biofeedback for Haptic Information , 2007 .

[18]  N. Birbaumer.  Motor Learning: Passing a Skill from One Hand to the Other , 2007, Current Biology.

[19]  Sjoerd J de Vries,et al.  Motor imagery and stroke rehabilitation: a critical discussion. , 2007, Journal of rehabilitation medicine.

[20]  Febo Cincotti,et al.  Vibrotactile Feedback for Brain-Computer Interface Operation , 2007, Comput. Intell. Neurosci..

[21]  D. McFarland,et al.  An auditory brain–computer interface (BCI) , 2008, Journal of Neuroscience Methods.

[22]  H. Krebs,et al.  Effects of Robot-Assisted Therapy on Upper Limb Recovery After Stroke: A Systematic Review , 2008, Neurorehabilitation and neural repair.

[23]  L. Der-Yeghiaian,et al.  Robot-based hand motor therapy after stroke. , 2007, Brain : a journal of neurology.

[24]  K.-R. Muller,et al.  Optimizing Spatial filters for Robust EEG Single-Trial Analysis , 2008, IEEE Signal Processing Magazine.

[25]  L. Cohen,et al.  Brain–computer interface in paralysis , 2008, Current opinion in neurology.

[26]  Ethan R. Buch,et al.  Think to Move: a Neuromagnetic Brain-Computer Interface (BCI) System for Chronic Stroke , 2008, Stroke.

[27]  N. Hogan,et al.  A working model of stroke recovery from rehabilitation robotics practitioners , 2009, Journal of NeuroEngineering and Rehabilitation.

[28]  J. Carmena,et al.  Emergence of a Stable Cortical Map for Neuroprosthetic Control , 2009, PLoS biology.

[29]  Rong Song,et al.  A Comparison Between Electromyography-Driven Robot and Passive Motion Device on Wrist Rehabilitation for Chronic Stroke , 2009, Neurorehabilitation and neural repair.

[30]  M. Grosse-Wentrup,et al.  Biased feedback in brain-computer interfaces , 2010, Journal of NeuroEngineering and Rehabilitation.

[31]  Cuntai Guan,et al.  Clinical study of neurorehabilitation in stroke using EEG-based motor imagery brain-computer interface with robotic feedback , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[32]  J. Peters,et al.  Closing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery , 2011, Journal of neural engineering.

[33]  B. Schoelkopf,et al.  Transition from the locked in to the completely locked-in state: A physiological analysis , 2011, Clinical Neurophysiology.

[34]  Aaron C. Koralek,et al.  Corticostriatal plasticity is necessary for learning intentional neuroprosthetic skills , 2012, Nature.