Deep Pinsker and James-Stein Neural Networks for Decoding Motor Intentions From Limited Data.

Journal Article (Journal Article)

Non-parametric regression has been shown to be useful in extracting relevant features from Local Field Potential (LFP) signals for decoding motor intentions. Yet, in many instances, brain-computer interfaces (BCIs) rely on simple classification methods, circumventing deep neural networks (DNNs) due to limited training data. This paper leverages the robustness of several important results in non-parametric regression to harness the potentials of deep learning in limited data setups. We consider a solution that combines Pinsker's theorem as well as its adaptively optimal counterpart due to James-Stein for feature extraction from LFPs, followed by a DNN for classifying motor intentions. We apply our approach to the problem of decoding eye movement intentions from LFPs collected in macaque cortex while the animals perform memory-guided visual saccades to one of eight target locations. The results demonstrate that a DNN classifier trained over the Pinsker features outperforms the benchmark method based on linear discriminant analysis (LDA) trained over the same features.

Full Text

Duke Authors

Cited Authors

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

Published Date

  • January 2021

Published In

Volume / Issue

  • 29 /

Start / End Page

  • 1058 - 1067

PubMed ID

  • 34038363

Electronic International Standard Serial Number (EISSN)

  • 1558-0210

International Standard Serial Number (ISSN)

  • 1534-4320

Digital Object Identifier (DOI)

  • 10.1109/tnsre.2021.3083755


  • eng