Wavelet Shrinkage and Thresholding Based Robust Classification for Brain-Computer Interface

Journal Article

A macaque monkey is trained to perform two different kinds of tasks, memory aided and visually aided. In each task, the monkey saccades to eight possible target locations. A classifier is proposed for direction decoding and task decoding based on local field potentials (LFP) collected from the prefrontal cortex. The LFP time-series data is modeled in a nonparametric regression framework, as a function corrupted by Gaussian noise. It is shown that if the function belongs to Besov bodies, then the proposed wavelet shrinkage and thresholding based classifier is robust and consistent. The classifier is then applied to the LFP data to achieve high decoding performance. The proposed classifier is also quite general and can be applied for the classification of other types of time-series data as well, not necessarily brain data.

Full Text

Duke Authors

Cited Authors

  • Banerjee, T; Choi, J; Pesaran, B; Ba, D; Tarokh, V

Published Date

  • September 10, 2018

Published In

Volume / Issue

  • 2018-April /

Start / End Page

  • 836 - 840

International Standard Serial Number (ISSN)

  • 1520-6149

Digital Object Identifier (DOI)

  • 10.1109/ICASSP.2018.8462321

Citation Source

  • Scopus