Multiresolution representations and data mining of neural spikes for brain-machine interfaces

Published

Journal Article

In brain-machine interface (BMI) applications, neural firing activities have been represented by spike counts with a fixed-width time bin. Adaptive models have been designed to utilize these bin counts for mapping the associated behavior which is typically 2D or 3D hand movement. However, the representation of the firing activities can be enriched by binning neural spikes with multiple time scales based on multiresolution analysis. This multiresolution representation of neural activities can provide more accurate prediction of the hand movement parameters. Data mining techniques must be applied to models using multiresolution representation in order to avoid overfitting. In this paper, we demonstrate that the multiresolution representation improves the performance of the linear model for BMIs compared to the model with the fixed-width time bin. © 2005 IEEE.

Full Text

Duke Authors

Cited Authors

  • Kim, SP; Carmena, JM; Nicolelis, MA; Principe, JC

Published Date

  • December 1, 2005

Published In

  • 2nd International Ieee Embs Conference on Neural Engineering

Volume / Issue

  • 2005 /

Start / End Page

  • 221 - 224

Digital Object Identifier (DOI)

  • 10.1109/CNE.2005.1419596

Citation Source

  • Scopus