Brain-Machine Interfaces for Assistive Robotics

Motor disability may be caused by many different conditions. The most common one is a cerebrovascular accident (CVA) which occurs when the blood supply to the brain stops [1]. If the length of this interruption is longer than several seconds, brain cells can die causing a permanent damage in the patient. When this damage occurs in the brain areas responsible for motor control, the patients may suffer permanent or temporal loss of mobility, coordination and control of their limbs. Another important cause of motor disability is due to spinal cord injury (SCI), which provokes the total loss of sensibility and movement capability below the level of the injury [2]. In this case, the patient assistance must be purely based on motor substitution, given that it is impossible to perform a rehabilitation procedure. Finally, less frequent illnesses and diseases may cause motor disfunctions, such as cerebral palsy, spina bifida, muscular dystrophy, amyotrophic lateral sclerosis (ALS) or central nervous system diseases such as Parkinson syndrome or Huntington disease.

[1]  L. Cohen,et al.  Brain–computer interfaces: communication and restoration of movement in paralysis , 2007, The Journal of physiology.

[2]  M. Horne,et al.  Interaction of embryonic cortical neurons on nanofibrous scaffolds for neural tissue engineering , 2007, Journal of neural engineering.

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

[4]  Enrique Hortal,et al.  Online classification of two mental tasks using a SVM-based BCI system , 2013, 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER).

[5]  Christa Neuper,et al.  Rehabilitation with Brain-Computer Interface Systems , 2008, Computer.

[6]  J. Knott,et al.  Regarding the American Electroencephalographic Society guidelines for standard electrode position nomenclature: a commentary on the proposal to change the 10-20 electrode designators. , 1993, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[7]  M Congedo,et al.  A review of classification algorithms for EEG-based brain–computer interfaces , 2007, Journal of neural engineering.

[8]  Chin-Sang Chung,et al.  Chapter 45 – Stroke and Other Neurovascular Disorders , 2007 .

[9]  M. Eimer The N2pc component as an indicator of attentional selectivity. , 1996, Electroencephalography and clinical neurophysiology.

[10]  S J Luck,et al.  Visual event-related potentials index focused attention within bilateral stimulus arrays. II. Functional dissociation of P1 and N1 components. , 1990, Electroencephalography and clinical neurophysiology.

[11]  Martin Eimer,et al.  The N2pc component and its links to attention shifts and spatially selective visual processing. , 2008, Psychophysiology.

[12]  Klaus-Robert Müller,et al.  Toward noninvasive brain-computer interfaces , 2006, IEEE Signal Process. Mag..

[13]  Rabab K Ward,et al.  A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals , 2007, Journal of neural engineering.

[14]  Jon A. Mukand,et al.  Neuronal ensemble control of prosthetic devices by a human with tetraplegia , 2006, Nature.

[15]  Andrés Úbeda,et al.  Visual evoked potential-based brain-machine interface applications to assist disabled people , 2012, Expert Syst. Appl..

[16]  José del R. Millán,et al.  Brain-Controlled Wheelchairs: A Robotic Architecture , 2013, IEEE Robotics & Automation Magazine.

[17]  G. R. Muller,et al.  "Virtual keyboard" controlled by spontaneous EEG activity , 2003 .

[18]  G. Ling Traumatic Brain Injury and Spinal Cord Injury , 2012 .

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

[20]  C. Neuper,et al.  Combining Brain–Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges , 2010, Front. Neurosci..

[21]  Michitaka Hirose,et al.  Non-target photo images in oddball paradigm improve EEG-based personal identification rates , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[22]  Wolfgang Rosenstiel,et al.  Nessi: An EEG-Controlled Web Browser for Severely Paralyzed Patients , 2007, Comput. Intell. Neurosci..

[23]  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.

[24]  Dean J. Krusienski,et al.  Ensemble SWLDA Classifiers for the P300 Speller , 2009, HCI.

[25]  Dean J Krusienski,et al.  A comparison of classification techniques for the P300 Speller , 2006, Journal of neural engineering.

[26]  R. Fazel-Rezai,et al.  Analysis of P300 Classifiers in Brain Computer Interface Speller , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[27]  B.Z. Allison,et al.  ERPs evoked by different matrix sizes: implications for a brain computer interface (BCI) system , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[28]  J Decety,et al.  Sensation of effort and duration of mentally executed actions. , 1991, Scandinavian journal of psychology.

[29]  Gernot R. Müller-Putz,et al.  Brain-computer interfaces for control of neuroprostheses: from synchronous to asynchronous mode of operation / Brain-Computer Interfaces zur Steuerung von Neuroprothesen: von der synchronen zur asynchronen Funktionsweise , 2006 .

[30]  M.B. Shamsollahi,et al.  Comparison between Effective Features Used for the Bayesian and the SVM Classifiers in BCI , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[31]  Iñaki Iturrate,et al.  A Noninvasive Brain-Actuated Wheelchair Based on a P300 Neurophysiological Protocol and Automated Navigation , 2009, IEEE Transactions on Robotics.

[32]  Andrés Úbeda,et al.  Control strategies of an assistive robot using a Brain-Machine Interface , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[33]  Andrés Úbeda,et al.  Assistive robot application based on an RFID control architecture and a wireless EOG interface , 2012, Robotics Auton. Syst..

[34]  Miguel A. L. Nicolelis,et al.  Actions from thoughts , 2001, Nature.

[35]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[36]  Andrew S. Whitford,et al.  Cortical control of a prosthetic arm for self-feeding , 2008, Nature.

[37]  E. Donchin,et al.  A P300-based brain–computer interface: Initial tests by ALS patients , 2006, Clinical Neurophysiology.

[38]  D M Durand,et al.  Suppression of axonal conduction by sinusoidal stimulation in rat hippocampus in vitro , 2007, Journal of neural engineering.

[39]  Andrés Úbeda,et al.  Shared control architecture based on RFID to control a robot arm using a spontaneous brain-machine interface , 2013, Robotics Auton. Syst..

[40]  Yoko Akiyama,et al.  The Development of BCI Using Alpha Waves for Controlling the Robot Arm , 2008, IEICE Trans. Commun..

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

[42]  G. Pfurtscheller,et al.  Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.

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

[44]  J. Wolpaw,et al.  Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects , 2009, IEEE Reviews in Biomedical Engineering.

[45]  Andrés Úbeda,et al.  Mental tasks-based brain-robot interface , 2010, Robotics Auton. Syst..

[46]  Jiang Wang,et al.  Motor Imagery BCI Research Based on Sample Entropy and SVM , 2012, 2012 Sixth International Conference on Electromagnetic Field Problems and Applications.