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This article is part of the series Trends in Brain Computer Interfaces.

Open Access Research Article

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

Sung-Phil Kim1*, Yadunandana N Rao2, Deniz Erdogmus3, Justin C Sanchez4, Miguel AL Nicolelis5 and Jose C Principe1

Author Affiliations

1 Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA

2 Motorola Inc., FL, USA

3 Department of Computer Science and Biomedical Engineering, Oregon Health & Science University, Beaverton, OR 97006, USA

4 Department of Pediatrics, Division of Neurology, University of Florida, Gainesville, FL 32611, USA

5 Department of Neurobiology, Center for Neuroengineering, Duke University, Durham, NC 27710, USA

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EURASIP Journal on Advances in Signal Processing 2005, 2005:829802  doi:10.1155/ASP.2005.3113

The electronic version of this article is the complete one and can be found online at: http://asp.eurasipjournals.com/content/2005/19/829802


Received:31 January 2004
Revisions received:23 March 2005
Published:17 November 2005

© 2005 Kim et al.

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.

Keywords:
brain-machine interfaces; nonnegative matrix factorization; spatiotemporal patterns; neural firing activity

Research Article