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.
| Original language | English |
|---|---|
| Title of host publication | 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 |
| Publisher | IEEE, Institute of Electrical and Electronics Engineers |
| Pages | 289-292 |
| Number of pages | 4 |
| ISBN (Print) | 9781457702167 |
| DOIs | |
| Publication status | Published - 2013 |
| Externally published | Yes |
| Event | International Conference of the IEEE Engineering in Medicine and Biology Society 2013 - Osaka International Convention Center, Osaka, Japan Duration: 3 Jul 2013 → 7 Jul 2013 Conference number: 35 |
Conference
| Conference | International Conference of the IEEE Engineering in Medicine and Biology Society 2013 |
|---|---|
| Abbreviated title | EMBC 2013 |
| Country | Japan |
| City | Osaka |
| Period | 3/07/13 → 7/07/13 |
Cite this
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Utilizing movement synergies to improve decoding performance for a brain machine interface. / Wong, Yan T.; Putrino, David; Weiss, Adam; Pesaran, Bijan.
2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013. IEEE, Institute of Electrical and Electronics Engineers, 2013. p. 289-292 6609494.Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
TY - GEN
T1 - Utilizing movement synergies to improve decoding performance for a brain machine interface
AU - Wong, Yan T.
AU - Putrino, David
AU - Weiss, Adam
AU - Pesaran, Bijan
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84886452161&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2013.6609494
DO - 10.1109/EMBC.2013.6609494
M3 - Conference Paper
SN - 9781457702167
SP - 289
EP - 292
BT - 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
PB - IEEE, Institute of Electrical and Electronics Engineers
ER -