Long-term use of a neural prosthesis in progressive paralysis

Brain–computer interfaces (BCIs) enable communication with others and allow machines or computers to be controlled in the absence of motor activity. Clinical studies evaluating neural prostheses in amyotrophic lateral sclerosis (ALS) patients have been performed; however, to date, no study has reported that ALS patients who progressed from locked-in syndrome (LIS), which has very limited voluntary movement, to a completely locked-in state (CLIS), characterized by complete loss of voluntary movements, were able to continue controlling neural prostheses. To clarify this, we used a BCI system to evaluate three late-stage ALS patients over 27 months. We employed steady-state visual evoked brain potentials elicited by flickering green and blue light-emitting diodes to control the BCI system. All participants reliably controlled the system throughout the entire period (median accuracy: 83.3%). One patient who progressed to CLIS was able to continue operating the system with high accuracy. Furthermore, this patient successfully used the system to respond to yes/no questions. Thus, this CLIS patient was able to operate a neuroprosthetic device, suggesting that the BCI system confers advantages for patients with severe paralysis, including those exhibiting complete loss of muscle movement.

[1]  Atsushi Maki,et al.  High cognitive function of an ALS patient in the totally locked-in state , 2008, Neuroscience Letters.

[2]  Abeer J. Hani,et al.  American Clinical Neurophysiology Society Guideline 2: Guidelines for Standard Electrode Position Nomenclature , 2016, The Neurodiagnostic journal.

[3]  Niels Birbaumer,et al.  Ideomotor silence: the case of complete paralysis and brain–computer interfaces (BCI) , 2012, Psychological Research.

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

[5]  Bernhard Schölkopf,et al.  Assessing attention and cognitive function in completely locked-in state with event-related brain potentials and epidural electrocorticography , 2014, Journal of neural engineering.

[6]  Marcello Massimini,et al.  Recovery of cortical effective connectivity and recovery of consciousness in vegetative patients , 2012, Brain : a journal of neurology.

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

[8]  Kouji Takano,et al.  A Non-Adhesive Solid-Gel Electrode for a Non-Invasive Brain–Machine Interface , 2012, Front. Neur..

[9]  N. Birbaumer,et al.  Brain–computer interfaces and communication in paralysis: Extinction of goal directed thinking in completely paralysed patients? , 2008, Clinical Neurophysiology.

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

[11]  N. Ramsey,et al.  Fully Implanted Brain-Computer Interface in a Locked-In Patient with ALS. , 2016, The New England journal of medicine.

[12]  G Müller-Putz,et al.  An independent SSVEP-based brain–computer interface in locked-in syndrome , 2014, Journal of neural engineering.

[13]  A. Maudsley,et al.  1H MRS of basal ganglia and thalamus in amyotrophic lateral sclerosis , 2011, NMR in biomedicine.

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

[15]  C. A. Ruf,et al.  Brain communication in the locked-in state. , 2013, Brain : a journal of neurology.

[16]  J. Wolpaw,et al.  A P300-based brain–computer interface for people with amyotrophic lateral sclerosis , 2008, Clinical Neurophysiology.

[17]  Chang-Hwan Im,et al.  Clinical feasibility of brain-computer interface based on steady-state visual evoked potential in patients with locked-in syndrome: Case studies. , 2017, Psychophysiology.

[18]  Toshio Shimizu,et al.  Marked preservation of the visual and olfactory pathways in ALS patients in a totally locked-in state. , 2015, Clinical neuropathology.

[19]  Clemens Brunner,et al.  Better than random? A closer look on BCI results , 2008 .

[20]  Martin Spueler,et al.  No Evidence for Communication in the Complete Locked-in State , 2018, bioRxiv.

[21]  R Weiss-Lambrou,et al.  Item Analysis of the Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST) , 2000, Assistive technology : the official journal of RESNA.

[22]  A. Nobre,et al.  Top-down modulation: bridging selective attention and working memory , 2012, Trends in Cognitive Sciences.

[23]  Marcia Grabowecky,et al.  Attention induces synchronization-based response gain in steady-state visual evoked potentials , 2007, Nature Neuroscience.

[24]  Toshihiro Kawase,et al.  Use of high-frequency visual stimuli above the critical flicker frequency in a SSVEP-based BMI , 2015, Clinical Neurophysiology.

[25]  Abeer J. Hani,et al.  American Clinical Neurophysiology Society Guideline 2: Guidelines for Standard Electrode Position Nomenclature , 2016, The Neurodiagnostic journal.

[26]  Shuichi Kato,et al.  Total manifestations of amyotrophic lateral sclerosis ALS in the totally locked-in state , 1989, Journal of the Neurological Sciences.

[27]  Sudhir Gupta,et al.  Case Studies , 2013, Journal of Clinical Immunology.

[28]  Kouji Takano,et al.  A region-based two-step P300-based brain–computer interface for patients with amyotrophic lateral sclerosis , 2014, Clinical Neurophysiology.

[29]  Anish A. Sarma,et al.  Clinical translation of a high-performance neural prosthesis , 2015, Nature Medicine.

[30]  Edmund C Lalor,et al.  A gaze independent hybrid-BCI based on visual spatial attention , 2017, Journal of neural engineering.

[31]  Eric W. Sellers,et al.  Noninvasive brain-computer interface enables communication after brainstem stroke , 2014, Science Translational Medicine.

[32]  Hartwig R. Siebner,et al.  SSVEP-modulation by covert and overt attention: Novel features for BCI in attention neuro-rehabilitation , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[33]  J. Wolpaw,et al.  Towards an independent brain–computer interface using steady state visual evoked potentials , 2008, Clinical Neurophysiology.