title: Neuromuscular electrical stimulation induced brain patterns to decode motor imagery.
abstract: OBJECTIVE
  Regardless of the paradigm used to implement a brain-computer interface (BCI), all systems suffer from BCI-inefficiency. In the case of patients the inefficiency can be high. Some solutions have been proposed to overcome this problem, however they have not been completely successful yet.
  
  
  METHODS
  EEG from 10 healthy users was recorded during neuromuscular electrical stimulation (NMES) of hands and feet and during motor imagery (MI) of the same limbs. Features and classifiers were computed using part of these data to decode MI.
  
  
  RESULTS
  Offline analyses showed that it was possible to decode MI using a classifier based on afferent patterns induced by NMES and even infer a better model than with MI data.
  
  
  CONCLUSIONS
  Afferent NMES motor patterns can support the calibration of BCI systems and be used to decode MI.
  
  
  CONCLUSIONS
  This finding might be a new way to train sensorimotor rhythm (SMR) based BCI systems for healthy users having difficulties to attain BCI control. It might also be an alternative to train MI-based BCIs for users who cannot perform real movements but have remaining afferents (ALS, stroke patients).
authors: Machine Learning Group, Berlin Institute of Technology, Berlin, Germany. carmen.vidaurre@tu-berlin.de
authors: C
authors: C
authors: Vidaurre
authors: J
authors: J
authors: Pascual
authors: A
authors: A
authors: Ramos-Murguialday
authors: R
authors: R
authors: Lorenz
authors: B
authors: B
authors: Blankertz
authors: N
authors: N
authors: Birbaumer
authors: K-R
authors: KR
authors: Müller
mesh_terms: D000704:Analysis of Variance; D001921:Brain / Q000502:physiology*; D062207:Brain-Computer Interfaces; D002138:Calibration; D004525:Efferent Pathways / Q000502:physiology; D004558:Electric Stimulation / Q000379:methods*; D004569:Electroencephalography; D056228:Feedback, Sensory / Q000502:physiology*; D006801:Humans; D007092:Imagination / Q000502:physiology*; D015233:Models, Statistical; D009068:Movement / Q000502:physiology; D012815:Signal Processing, Computer-Assisted
keywords: Afferent patterns; BCI-inefficency; Efferent pattern classification; Motor imagery; Neuromuscular electrical stimulation