Detecting intention to walk in stroke patients from pre-movement EEG correlates

BackgroundMost studies in the field of brain-computer interfacing (BCI) for lower limbs rehabilitation are carried out with healthy subjects, even though insights gained from healthy populations may not generalize to patients in need of a BCI.MethodsWe investigate the ability of a BCI to detect the intention to walk in stroke patients from pre-movement EEG correlates. Moreover, we also investigated how the motivation of the patients to execute a task related to the rehabilitation therapy affects the BCI accuracy. Nine chronic stroke patients performed a self-initiated walking task during three sessions, with an intersession interval of one week.ResultsUsing a decoder that combines temporal and spectral sparse classifiers we detected pre-movement state with an accuracy of 64 % in a range between 18 % and 85.2 %, with the chance level at 4 %. Furthermore, we found a significantly strong positive correlation (r = 0.561, p = 0.048) between the motivation of the patients to perform the rehabilitation related task and the accuracy of the BCI detector of their intention to walk.ConclusionsWe show that a detector based on temporal and spectral features can be used to classify pre-movement state in stroke patients. Additionally, we found that patients’ motivation to perform the task showed a strong correlation to the attained detection rate of their walking intention.

[1]  H. Kornhuber,et al.  [CHANGES IN THE BRAIN POTENTIAL IN VOLUNTARY MOVEMENTS AND PASSIVE MOVEMENTS IN MAN: READINESS POTENTIAL AND REAFFERENT POTENTIALS]. , 1965, Pflugers Archiv fur die gesamte Physiologie des Menschen und der Tiere.

[2]  G. Pfurtscheller,et al.  Evaluation of event-related desynchronization (ERD) preceding and following voluntary self-paced movement. , 1979, Electroencephalography and clinical neurophysiology.

[3]  S. Holm.  A Simple Sequentially Rejective Multiple Test Procedure , 1979 .

[4]  D. Winter,et al.  EMG profiles during normal human walking: stride-to-stride and inter-subject variability. , 1987, Electroencephalography and clinical neurophysiology.

[5]  M. Cowan,et al.  American Heart Association. , 2018, P & T : a peer-reviewed journal for formulary management.

[6]  S. Bandinelli,et al.  Recovery of Functional Status After Stroke: A Postrehabilitation Follow‐up Study , 1993, Stroke.

[7]  M. Dam,et al.  The Effects of Long‐term Rehabilitation Therapy on Poststroke Hemiplegic Patients , 1993, Stroke.

[8]  S Giaquinto,et al.  EEG Recordings in the Course of Recovery From Stroke , 1994, Stroke.

[9]  L. Elias,et al.  Footedness is a better predictor than is handedness of emotional lateralization , 1998, Neuropsychologia.

[10]  O. Bertrand,et al.  Oscillatory gamma activity in humans and its role in object representation , 1999, Trends in Cognitive Sciences.

[11]  J. B. Green,et al.  High-resolution EEG in poststroke hemiparesis can identify ipsilateral generators during motor tasks. , 1999, Stroke.

[12]  E. Deci,et al.  Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. , 2000, The American psychologist.

[13]  Aapo Hyvärinen,et al.  A Fast Fixed-Point Algorithm for Independent Component Analysis of Complex Valued Signals , 2000, Int. J. Neural Syst..

[14]  G. Pfurtscheller,et al.  Event-related dynamics of cortical rhythms: frequency-specific features and functional correlates. , 2001, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[15]  R D Pascual-Marqui,et al.  Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. , 2002, Methods and findings in experimental and clinical pharmacology.

[16]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[17]  H. Kornhuber,et al.  Hirnpotentialänderungen bei Willkürbewegungen und passiven Bewegungen des Menschen: Bereitschaftspotential und reafferente Potentiale , 1965, Pflüger's Archiv für die gesamte Physiologie des Menschen und der Tiere.

[18]  Lorie G Richards,et al.  Physical therapy during stroke rehabilitation for people with different walking abilities. , 2005, Archives of physical medicine and rehabilitation.

[19]  Michael Voigt,et al.  Movement-related parameters modulate cortical activity during imaginary isometric plantar-flexions , 2006, Experimental Brain Research.

[20]  Bernhard Graimann,et al.  Quantification and visualization of event-related changes in oscillatory brain activity in the time-frequency domain. , 2006, Progress in brain research.

[21]  M. Hallett,et al.  What is the Bereitschaftspotential? , 2006, Clinical Neurophysiology.

[22]  Hiroshi Shibasaki,et al.  How does the brain respond to unimodal and bimodal sensory demand in movement of the lower extremity? , 2007, Experimental Brain Research.

[23]  Marie-Françoise Lucas,et al.  Optimization of wavelets for classification of movement-related cortical potentials generated by variation of force-related parameters , 2007, Journal of Neuroscience Methods.

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

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

[26]  Gert Pfurtscheller,et al.  Overt foot movement detection in one single Laplacian EEG derivation , 2008, Journal of Neuroscience Methods.

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

[28]  N. Birbaumer,et al.  The Influence of Psychological State and Motivation on Brain–Computer Interface Performance in Patients with Amyotrophic Lateral Sclerosis – a Longitudinal Study , 2010, Front. Neuropharma..

[29]  D. Farina,et al.  Detection of movement intention from single-trial movement-related cortical potentials , 2011, Journal of neural engineering.

[30]  Trevor J. Hastie,et al.  Sparse Discriminant Analysis , 2011, Technometrics.

[31]  Alessandro Presacco,et al.  Towards a non-invasive brain-machine interface system to restore gait function in humans , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[32]  R. Osu,et al.  Event related desynchronization-modulated functional electrical stimulation system for stroke rehabilitation: A feasibility study , 2012, Journal of NeuroEngineering and Rehabilitation.

[33]  Eduardo Rocon de Lima,et al.  An EEG-Based Design for the Online Detection of Movement Intention , 2011, IWANN.

[34]  M. Hallett,et al.  Prediction of human voluntary movement before it occurs , 2011, Clinical Neurophysiology.

[35]  M. Molinari,et al.  Rehabilitation of gait after stroke: a review towards a top-down approach , 2011, Journal of NeuroEngineering and Rehabilitation.

[36]  Klaus-Robert Müller,et al.  Introduction to machine learning for brain imaging , 2011, NeuroImage.

[37]  C. Nam,et al.  Movement imagery-related lateralization of event-related (de)synchronization (ERD/ERS): Motor-imagery duration effects , 2011, Clinical Neurophysiology.

[38]  Peter Desain,et al.  Feasibility of measuring event Related Desynchronization with electroencephalography during walking , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[39]  Garrison W. Greenwood,et al.  Advances in Computational Intelligence , 2012, Lecture Notes in Computer Science.

[40]  Pablo Rabanal,et al.  Advances in Computational Intelligence , 2012, Communications in Computer and Information Science.

[41]  Dario Farina,et al.  Detection of movement-related cortical potentials based on subject-independent training , 2013, Medical & Biological Engineering & Computing.

[42]  Christa Neuper,et al.  Level of participation in robotic-assisted treadmill walking modulates midline sensorimotor EEG rhythms in able-bodied subjects , 2012, NeuroImage.

[43]  J. Millán,et al.  Detection of self-paced reaching movement intention from EEG signals , 2012, Front. Neuroeng..

[44]  Luis Montesano,et al.  Continuous decoding of motor attempt and motor imagery from EEG activity in spinal cord injury patients , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[45]  K. Müller,et al.  Psychological predictors of SMR-BCI performance , 2012, Biological Psychology.

[46]  Luis Montesano,et al.  Continuous decoding of intention to move from contralesional hemisphere brain oscillations in severely affected chronic stroke patients , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[47]  J. Millán,et al.  Single trial analysis of slow cortical potentials: a study on anticipation related potentials , 2013, Journal of neural engineering.

[48]  Saeid Sanei,et al.  Constrained Blind Source Extraction of Readiness Potentials From EEG , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[49]  L. Cohen,et al.  Brain–machine interface in chronic stroke rehabilitation: A controlled study , 2013, Annals of neurology.

[50]  Virginia R. de Sa,et al.  Single-trial classification of gait and point movement preparation from human EEG , 2013, Front. Neurosci..

[51]  Jose L. Contreras-Vidal,et al.  Sitting and standing intention can be decoded from scalp EEG recorded prior to movement execution , 2014, Front. Neurosci..

[52]  Max Wintermark,et al.  Recommendations for the Management of Cerebral and Cerebellar Infarction With Swelling: A Statement for Healthcare Professionals From the American Heart Association/American Stroke Association , 2014, Stroke.

[53]  Wim L. C. Rutten,et al.  Temporal evolution of event-related desynchronization in acute stroke: A pilot study , 2014, Clinical Neurophysiology.

[54]  M. Mahadevappa,et al.  Single channel Event related (De)synchronization(ERD/ERS) analysis of motor execution in stroke affected foot drop subjects , 2014, 2014 International Conference on Medical Imaging, m-Health and Emerging Communication Systems (MedCom).

[55]  Á. Gil-Agudo,et al.  Continuous decoding of movement intention of upper limb self-initiated analytic movements from pre-movement EEG correlates , 2014, Journal of NeuroEngineering and Rehabilitation.

[56]  Jose L Pons,et al.  Detection of the Onset of Voluntary Movements Based on the Combination of ERD and BP Cortical Patterns , 2014 .

[57]  Andreea Ioana Sburlea,et al.  Continuous detection of the self-initiated walking pre-movement state from EEG correlates without session-to-session recalibration , 2015, Journal of neural engineering.

[58]  D. Farina,et al.  A brain–computer interface for single-trial detection of gait initiation from movement related cortical potentials , 2015, Clinical Neurophysiology.