Connectivity analysis as a novel approach to motor decoding for prosthesis control

Heather L. Benz, Huaijian Zhang, anastasios Bezerianos, Soumyadipta Acharya, Nathan E. Crone, Xioaxiang Zheng, Nitish V. Thakor

Research output: Contribution to journalArticlepeer-review

32 Scopus citations


The use of neural signals for prosthesis control is an emerging frontier of research to restore lost function to amputees and the paralyzed. Electrocorticography (ECoG) brain-machine interfaces (BMI) are an alternative to EEG and neural spiking and local field potential BMI approaches. Conventional ECoG BMIs rely on spectral analysis at specific electrode sites to extract signals for controlling prostheses. We compare traditional features with information about the connectivity of an ECoG electrode network. We use time-varying dynamic Bayesian networks (TV-DBN) to determine connectivity between ECoG channels in humans during a motor task. We show that, on average, TV-DBN connectivity decreases from baseline preceding movement and then becomes negative, indicating an alteration in the phase relationship between electrode pairs. In some subjects, this change occurs preceding and during movement, before changes in low or high frequency power. We tested TV-DBN output in a hand kinematic decoder and obtained an average correlation coefficient (r 2) between actual and predicted joint angle of 0.40, and as high as 0.66 in one subject. This result compares favorably with spectral feature decoders, for which the average correlation coefficient was 0.13. This work introduces a new feature set based on connectivity and demonstrates its potential to improve ECoG BMI accuracy.

Original languageEnglish (US)
Article number6072267
Pages (from-to)143-152
Number of pages10
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Issue number2
StatePublished - Mar 2012


  • Brain-computer interfaces
  • connectivity analysis
  • motor control
  • time-varying dynamic Bayesian networks

ASJC Scopus subject areas

  • Internal Medicine
  • General Neuroscience
  • Biomedical Engineering
  • Rehabilitation


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