Low-rank representation of neural activity and detection of submovements

Young Hwan Chang, Mo Chen, Suraj Gowda, Simon A. Overduin, Jose M. Carmena, Claire Tomlin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


In this study, Robust Principal Component Analysis (RPCA) is applied to neural spike datasets to extract neural signatures that signify the onset of submovements, a type of motor primitive. Given neural activity recorded from rhesus macaques during a set of reaches between targets in a horizontal plane, we aim to identify common event-related neural features and validate sub movement-based motor primitives inferred from the hand velocity profiles. Such features represent common dynamic patterns across many experimental trials and may be used as a signature to detect discrete events such as submovement onset. We present RPCA, a method well suited for extracting data matrices' low-rank component and this method allows (1) removal of task-irrelevant signal from data, (2) identification of task-related dynamic patterns, and (3) detection of sub movements. We also explored using the Random Projection (RP) technique and applying RP to data prior to applying RPCA improved the sub movement prediction performance by de-sparsifying neural data while preserving certain statistical characteristics of aggregate neural activity.

Original languageEnglish (US)
Title of host publication2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Print)9781467357173
StatePublished - 2013
Externally publishedYes
Event52nd IEEE Conference on Decision and Control, CDC 2013 - Florence, Italy
Duration: Dec 10 2013Dec 13 2013

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370


Other52nd IEEE Conference on Decision and Control, CDC 2013

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization


Dive into the research topics of 'Low-rank representation of neural activity and detection of submovements'. Together they form a unique fingerprint.

Cite this