EURASIP Journal on Applied Signal Processing
Volume 2005 (2005), Issue 19, Pages 3113-3121
doi:10.1155/ASP.2005.3113

Determining Patterns in Neural Activity for Reaching Movements Using Nonnegative Matrix Factorization

Sung-Phil Kim,1 Yadunandana N. Rao,2 Deniz Erdogmus,3 Justin C. Sanchez,4 Miguel A. L. Nicolelis,5 and Jose C. Principe1

1Department of Electrical and Computer Engineering, University of Florida, Gainesville 32611, FL, USA
2Motorola Inc., FL, USA
3Department of Computer Science and Biomedical Engineering, Oregon Health & Science University, Beaverton 97006, OR, USA
4Department of Pediatrics, Division of Neurology, University of Florida, Gainesville 32611, FL, USA
5Department of Neurobiology, Center for Neuroengineering, Duke University, Durham 27710, NC, USA

Received 31 January 2004; Revised 23 March 2005

Copyright © 2005 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We propose the use of nonnegative matrix factorization (NMF) as a model-independent methodology to analyze neural activity. We demonstrate that, using this technique, it is possible to identify local spatiotemporal patterns of neural activity in the form of sparse basis vectors. In addition, the sparseness of these bases can help infer correlations between cortical firing patterns and behavior. We demonstrate the utility of this approach using neural recordings collected in a brain-machine interface (BMI) setting. The results indicate that, using the NMF analysis, it is possible to improve the performance of BMI models through appropriate pruning of inputs.