Neuromuscular electrical stimulation induced brain patterns to decode motor imagery

C. Vidaurre, J. Pascual, A. Ramos-Murguialday, R. Lorenz, B. Blankertz, N. Birbaumer, K. R. Müller

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


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. Conclusion: Afferent NMES motor patterns can support the calibration of BCI systems and be used to decode MI. Significance: 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).

Original languageEnglish
Pages (from-to)1824-1834
Number of pages11
JournalClinical Neurophysiology
Issue number9
Publication statusPublished - 2013 Sept


  • Afferent patterns
  • BCI-inefficency
  • Efferent pattern classification
  • Motor imagery
  • Neuromuscular electrical stimulation

ASJC Scopus subject areas

  • Sensory Systems
  • Neurology
  • Clinical Neurology
  • Physiology (medical)


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