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Open Access

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

  • Sung-Phil Kim1Email author,
  • Yadunandana N. Rao2,
  • Deniz Erdogmus3,
  • Justin C. Sanchez4,
  • Miguel A. L. Nicolelis5 and
  • Jose C. Principe1
EURASIP Journal on Advances in Signal Processing20052005:829802

Received: 31 January 2004

Published: 17 November 2005


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 and phrases

brain-machine interfacesnonnegative matrix factorizationspatiotemporal patternsneural firing activity

Authors’ Affiliations

Department of Electrical and Computer Engineering, University of Florida, Gainesville, USA
Motorola Inc., FL, USA
Department of Computer Science and Biomedical Engineering, Oregon Health & Science University, Beaverton, USA
Department of Pediatrics, Division of Neurology, University of Florida, Gainesville, USA
Department of Neurobiology, Center for Neuroengineering, Duke University, Durham, USA


© Kim et al. 2005