Deep James-Stein Neural Networks for Brain-Computer Interfaces

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publication2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1339-1343
Number of pages5
ISBN (Electronic)9781509066315
DOIs
StatePublished - May 2020
Event2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain
Duration: May 4 2020May 8 2020

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2020-May
ISSN (Print)1520-6149

Conference

Conference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
CountrySpain
CityBarcelona
Period5/4/205/8/20

Keywords

  • Brain-computer interfaces
  • James-Stein estimation
  • deep neural networks
  • nonparametric regression

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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