System derived spatial-temporal CNN for high-density fNIRS BCI

Robin Dale, Thomas D. O'Sullivan, Scott Howard, Felipe Orihuela-Espina, Hamid Dehghani

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An intuitive and generalisable approach to spatial-temporal feature extraction for high-density (HD) functional Near-Infrared Spectroscopy (fNIRS) brain-computer interface (BCI) is proposed, demonstrated here using Frequency-Domain (FD) fNIRS for motor-task classification. Enabled by the HD probe design, layered topographical maps of Oxy/deOxy Haemoglobin changes are used to train a 3D convolutional neural network (CNN), enabling simultaneous extraction of spatial and temporal features. The proposed spatial-temporal CNN is shown to effectively exploit the spatial relationships in HD fNIRS measurements to improve the classification of the functional haemodynamic response, achieving an average F1 score of 0.69 across seven subjects in a mixed subjects training scheme, and improving subject-independent classification as compared to a standard temporal CNN.
Original languageEnglish
Article number10073629
Pages (from-to)85-95
Number of pages11
JournalIEEE Open Journal of Engineering in Medicine and Biology
Early online date16 Mar 2023
Publication statusPublished - 22 May 2023

Bibliographical note

Funding Information:
This work was supported by the National Institute of Biomedical Imaging, and Bioengineering (NIBIB) of the National Institutes of Health (NIH) under Award R01EB029595.

Publisher Copyright:
© 2020 IEEE.


  • Functional near-infrared spectroscopy
  • Feature extraction
  • Probes
  • Convolutional neural networks
  • Task analysis
  • Hardware
  • Biological neural networks

ASJC Scopus subject areas

  • Biomedical Engineering


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