ERD-based online brain-machine interfaces (BMI) in the context of neurorehabilitation: Optimizing BMI learning and performance

Surjo R. Soekadar, Matthias Witkowski, Jürgen Mellinger, Ander Ramos, Niels Birbaumer, Leonardo G. Cohen

Research output: Contribution to journalArticlepeer-review


Event-related desynchronization (ERD) of sensori-motor rhythms (SMR) can be used for online brain-machine interface (BMI) control, but yields challenges related to the stability of ERD and feedback strategy to optimize BMI learning. Here, we compared two approaches to this challenge in 20 right-handed healthy subjects (HS, five sessions each, S1-S5) and four stroke patients (SP, 15 sessions each, S1-S15). ERD was recorded from a 275-sensor MEG system. During daily training, motor imagery-induced ERD led to visual and proprioceptive feedback delivered through an orthotic device attached to the subjects' hand and fingers. Group A trained with a heterogeneous reference value (RV) for ERD detection with binary feedback and Group B with a homogenous RV and graded feedback (10 HS and 2 SP in each group). HS in Group B showed better BMI performance than Group A (p <0.001) and improved BMI control from S1 to S5 ( p=0.012) while Group A did not. In spite of the small n, SP in Group B showed a trend for a higher BMI performance (p = 0.06) and learning was significantly better (p <0.05). Using a homogeneous RV and graded feedback led to improved modulation of ipsilesional activity resulting in superior BMI learning relative to use of a heterogeneous RV and binary feedback.

Original languageEnglish
Article number6035989
Pages (from-to)542-549
Number of pages8
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Issue number5
Publication statusPublished - Oct 2011


  • Brain-machine nterface
  • event-related desynchronization
  • neurorehabilitation
  • stroke

ASJC Scopus subject areas

  • Neuroscience(all)
  • Computer Science Applications
  • Biomedical Engineering
  • Medicine(all)

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