Deep James-Stein Neural Networks for Brain-Computer Interfaces

Conference Paper

Nonparametric regression has proven to be successful in extracting features from limited data in neurological applications. However, due to data scarcity, most brain-computer interfaces still rely on linear classifiers. This work leverages the robustness of the James-Stein theorem in nonparametric regression to harness the potentials of deep learning and foster its successful application in neural engineering with small data sets. We propose a novel method that combines James-Stein regression for feature extraction, and deep neural network for decoding; we refer to the architecture as deep James-Stein neural network (DJSNN). We apply the DJSNN to decode eye movement goals in a memory-guided visual saccades to one of eight target locations. The results demonstrate that the DJSNN outperforms existing methods by a substantial margin, especially at deep cortical sites.

Full Text

Duke Authors

Cited Authors

  • Angjelichinoski, M; Soltani, M; Choi, J; Pesaran, B; Tarokh, V

Published Date

  • May 1, 2020

Published In

Volume / Issue

  • 2020-May /

Start / End Page

  • 1339 - 1343

International Standard Serial Number (ISSN)

  • 1520-6149

International Standard Book Number 13 (ISBN-13)

  • 9781509066315

Digital Object Identifier (DOI)

  • 10.1109/ICASSP40776.2020.9053694

Citation Source

  • Scopus