Utilizing movement synergies to improve decoding performance for a brain machine interface

Yan T. Wong, David Putrino, Adam Weiss, Bijan Pesaran

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

10 Citations (Scopus)


A major challenge facing the development of high degree of freedom (DOF) brain machine interface (BMI) devices is a limited ability to provide prospective users with independent control of many DOFs when using a complex prosthesis. It has been previously shown that a large range of complex hand postures can be replicated using a relatively low number of movement synergies. Thus, a high DOF joint space, such as the one the hand resides in, may be decomposed via principal component analysis (PCA) into a lower DOF (eigen-reach) space that contains most of the variance of the original movements. By decoding in this eigen-reach space, BMI users need only control a few eigen-reach values to be able to make movements using all DOFs in the arm and hand. In this paper we examine how using PCA before decoding neural activity may lead to improvements in decoding performance.

Original languageEnglish
Title of host publication2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages4
ISBN (Print)9781457702167
Publication statusPublished - 2013
Externally publishedYes
EventInternational Conference of the IEEE Engineering in Medicine and Biology Society 2013 - Osaka International Convention Center, Osaka, Japan
Duration: 3 Jul 20137 Jul 2013
Conference number: 35th
https://ieeexplore.ieee.org/xpl/conhome/6596169/proceeding (Proceedings)


ConferenceInternational Conference of the IEEE Engineering in Medicine and Biology Society 2013
Abbreviated titleEMBC 2013
Internet address

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