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

Marko Angjelichinoski, Mohammadreza Soltani, John Choi, Bijan Pesaran, Vahid Tarokh

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article number9440925
Pages (from-to)1058-1067
Number of pages10
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
StatePublished - 2021


  • Neural decoding
  • brain-computer interfaces
  • local field potentials
  • neural networks
  • non-parametric regression

ASJC Scopus subject areas

  • Internal Medicine
  • General Neuroscience
  • Biomedical Engineering
  • Rehabilitation


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