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

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.

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Correspondence to Sung-Phil Kim.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Kim, SP., Rao, Y.N., Erdogmus, D. et al. Determining Patterns in Neural Activity for Reaching Movements Using Nonnegative Matrix Factorization. EURASIP J. Adv. Signal Process. 2005, 829802 (2005). https://doi.org/10.1155/ASP.2005.3113

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Keywords and phrases

  • brain-machine interfaces
  • nonnegative matrix factorization
  • spatiotemporal patterns
  • neural firing activity