Utilizing movement synergies to improve decoding performance for a brain machine interface
Year: 2013
Type: conference-paper
Abstract: 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.
Authors: Yan T. Wong, David Putrino, Adam Weiss, Bijan Pesaran
Institution: New York University
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
Funders: Wellcome Trust, National Eye Institute
Award: G2746281356
OpenAlex ID: W1977075348