The publication detail shows the title, authors (with indicators showing other profiled authors), information on the publishing organization, abstract and a link to the article in PubMed. This abstract is what is used to create the fingerprint of the publication. If any grants are referenced by the publication, they will be listed here as well.
Connectivity analysis as a novel approach to motor decoding for prosthesis control.
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA. firstname.lastname@example.org
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society 2012;20(2):143-52.
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.
This section shows information related to the publication - computed using the fingerprint of the publication - including related publications, related experts and related grants with fingerprints representing significant amounts of overlap between their fingerprint and this publication. The red dots indicate whether those experts or terms appear within the publication, thereby showing potential and actual connections.
Eric C Leuthardt; Zac Freudenberg; David Bundy; Jarod RolandNeurosurgical focus 2009;27(1):E10.
Matthew S Fifer; Soumyadipta Acharya; Heather L Benz; Mohsen Mollazadeh; Nathan E Crone; Nitish V ThakorIEEE pulse 2012;3(1):38-42.
Charles C Della Santina; Americo A Migliaccio; Amit H PatelIEEE transactions on bio-medical engineering 2007;54(6 Pt 1):1016-30.
Appears in this Publication
Author of this Publication