title: Detecting intention to walk in stroke patients from pre-movement EEG correlates.
abstract: BACKGROUND
  Most 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.
  
  
  METHODS
  We 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.
  
  
  RESULTS
  Using 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.
  
  
  CONCLUSIONS
  We 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.
authors.affiliation: Bit & Brain Technologies S.L., Calle Maria de Luna 11, nave 4, Zaragoza, 50018, Spain. andreea.sburlea@bitbrain.es.
authors.forename: Andreea Ioana
authors.initials: AI
authors.lastname: Sburlea
authors.affiliation: University of Zaragoza, Institute of Investigation in Engineering of Aragon (I3A), Building I+D+i, Mariano Esquillor, Zaragoza, 50018, Spain. montesano@unizar.es.
authors.forename: Luis
authors.initials: L
authors.lastname: Montesano
authors.affiliation: Department of Physiotherapy, Occupational therapy, Rehabilitation and Physical Medicine, Faculty of Health Sciences, Alcorcon, Madrid, Spain. roberto.cano@urjc.es.
authors.forename: Roberto
authors.initials: R
authors.lastname: Cano de la Cuerda
authors.affiliation: Department of Physiotherapy, Occupational therapy, Rehabilitation and Physical Medicine, Faculty of Health Sciences, Alcorcon, Madrid, Spain. isabel.alguacil@urjc.es.
authors.forename: Isabel Maria
authors.initials: IM
authors.lastname: Alguacil Diego
authors.affiliation: Department of Physiotherapy, Occupational therapy, Rehabilitation and Physical Medicine, Faculty of Health Sciences, Alcorcon, Madrid, Spain. juan.miangolarra@urjc.es.
authors.forename: Juan Carlos
authors.initials: JC
authors.lastname: Miangolarra-Page
authors.affiliation: Bit & Brain Technologies S.L., Calle Maria de Luna 11, nave 4, Zaragoza, 50018, Spain. jminguez@unizar.es.|University of Zaragoza, Institute of Investigation in Engineering of Aragon (I3A), Building I+D+i, Mariano Esquillor, Zaragoza, 50018, Spain. jminguez@unizar.es.
authors.forename: Javier
authors.initials: J
authors.lastname: Minguez
mesh_terms: D000328:Adult; D000368:Aged; D062207:Brain-Computer Interfaces; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D033182:Intention; D008297:Male; D008875:Middle Aged; D009042:Motivation / Q000502:physiology; D020521:Stroke / Q000523:psychology; D000071939:Stroke Rehabilitation; D016138:Walking / Q000502:physiology / Q000523:psychology