Closing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery

The combination of brain-computer interfaces (BCIs) with robot-assisted physical therapy constitutes a promising approach to neurorehabilitation of patients with severe hemiparetic syndromes caused by cerebrovascular brain damage (e.g. stroke) and other neurological conditions. In such a scenario, a key aspect is how to reestablish the disrupted sensorimotor feedback loop. However, to date it is an open question how artificially closing the sensorimotor feedback loop influences the decoding performance of a BCI. In this paper, we answer this issue by studying six healthy subjects and two stroke patients. We present empirical evidence that haptic feedback, provided by a seven degrees of freedom robotic arm, facilitates online decoding of arm movement intention. The results support the feasibility of future rehabilitative treatments based on the combination of robot-assisted physical therapy with BCIs.

[1]  H. B. Mann,et al.  On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other , 1947 .

[2]  W. Kruskal,et al.  Use of Ranks in One-Criterion Variance Analysis , 1952 .

[3]  W. Garraway,et al.  Proprioception and spatial neglect after stroke. , 1983, Age and ageing.

[4]  E. Donchin,et al.  Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. , 1988, Electroencephalography and clinical neurophysiology.

[5]  A. Tamhane,et al.  Multiple Comparison Procedures , 1989 .

[6]  M. Jeannerod.  Mental imagery in the motor context , 1995, Neuropsychologia.

[7]  G. Pfurtscheller,et al.  EEG-based discrimination between imagination of right and left hand movement. , 1997, Electroencephalography and clinical neurophysiology.

[8]  J R Wolpaw,et al.  Spatial filter selection for EEG-based communication. , 1997, Electroencephalography and clinical neurophysiology.

[9]  John Platt,et al.  Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods , 1999 .

[10]  H. Flor,et al.  A spelling device for the paralysed , 1999, Nature.

[11]  E Donchin,et al.  Brain-computer interface technology: a review of the first international meeting. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[12]  J. Mouriño,et al.  Recognition of imagined hand movements with low resolution surface Laplacian and linear classifiers. , 2001, Medical engineering & physics.

[13]  Alexander J. Smola,et al.  Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.

[14]  G. R. Muller,et al.  Event-related beta EEG changes during wrist movements induced by functional electrical stimulation of forearm muscles in man , 2003, Neuroscience Letters.

[15]  Anthony Widjaja,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.

[16]  J. Wolpaw,et al.  EMG contamination of EEG: spectral and topographical characteristics , 2003, Clinical Neurophysiology.

[17]  José del R. Millán,et al.  Noninvasive brain-actuated control of a mobile robot by human EEG , 2004, IEEE Transactions on Biomedical Engineering.

[18]  N. Birbaumer,et al.  BCI2000: a general-purpose brain-computer interface (BCI) system , 2004, IEEE Transactions on Biomedical Engineering.

[19]  H. C. Dijkerman,et al.  Does motor imagery training improve hand function in chronic stroke patients? A pilot study , 2004, Clinical rehabilitation.

[20]  G. Kwakkel,et al.  The impact of physical therapy on functional outcomes after stroke: what's the evidence? , 2004, Clinical rehabilitation.

[21]  Carl E. Rasmussen,et al.  Evaluating Predictive Uncertainty Challenge , 2005, MLCW.

[22]  Dennis J. McFarland,et al.  Brain-computer interface (BCI) operation: signal and noise during early training sessions , 2005, Clinical Neurophysiology.

[23]  J. Wolpaw,et al.  Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface , 2005, Neurology.

[24]  Kazuo Tanaka,et al.  Electroencephalogram-based control of an electric wheelchair , 2005, IEEE Transactions on Robotics.

[25]  Robert Riener,et al.  Robot-aided neurorehabilitation of the upper extremities , 2005, Medical and Biological Engineering and Computing.

[26]  Bernhard Schölkopf,et al.  Classifying Event-Related Desynchronization in EEG, ECoG and MEG Signals , 2006, DAGM-Symposium.

[27]  S. Page,et al.  Mental Practice in Chronic Stroke: Results of a Randomized, Placebo-Controlled Trial , 2007, Stroke.

[28]  G. Pfurtscheller,et al.  Event-related beta EEG-changes during passive and attempted foot movements in paraplegic patients , 2007, Brain Research.

[29]  J. Wolpaw,et al.  Brain–computer interfaces in neurological rehabilitation , 2008, The Lancet Neurology.

[30]  Kazuo Tanaka,et al.  Electroencephalogram-based control of an electric wheelchair , 2005, IEEE Transactions on robotics.

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

[32]  C. Braun,et al.  Hand Movement Direction Decoded from MEG and EEG , 2008, The Journal of Neuroscience.

[33]  Rajesh P. N. Rao,et al.  Generalized Features for Electrocorticographic BCIs , 2008, IEEE Transactions on Biomedical Engineering.

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

[35]  T. Murphy,et al.  Plasticity during stroke recovery: from synapse to behaviour , 2009, Nature Reviews Neuroscience.

[36]  Cuntai Guan,et al.  A clinical study of motor imagery-based brain-computer interface for upper limb robotic rehabilitation , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[37]  Xuedong Chen,et al.  Decoding human motor activity from EEG single trials for a discrete two-dimensional cursor control , 2009, Journal of neural engineering.

[38]  S. Halder,et al.  Proprioceptive feedback in BCI , 2009, 2009 4th International IEEE/EMBS Conference on Neural Engineering.

[39]  Moritz Grosse-Wentrup,et al.  Beamforming in Noninvasive Brain–Computer Interfaces , 2009, IEEE Transactions on Biomedical Engineering.

[40]  A. Tamhane,et al.  Multiple Comparison Procedures , 2009 .

[41]  Bernhard Schölkopf,et al.  Closing the sensorimotor loop: Haptic feedback facilitates decoding of arm movement imagery , 2010, 2010 IEEE International Conference on Systems, Man and Cybernetics.

[42]  Lalit Kalra,et al.  Stroke rehabilitation 2009: old chestnuts and new insights. , 2010, Stroke.

[43]  Monica A. Perez,et al.  Neural interface technology for rehabilitation: exploiting and promoting neuroplasticity. , 2010, Physical medicine and rehabilitation clinics of North America.

[44]  Rajesh P. N. Rao,et al.  Cortical activity during motor execution, motor imagery, and imagery-based online feedback , 2010, Proceedings of the National Academy of Sciences.

[45]  Nicholas G Hatsopoulos,et al.  Incorporating Feedback from Multiple Sensory Modalities Enhances Brain–Machine Interface Control , 2010, The Journal of Neuroscience.

[46]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.