System-driven convolutional feature extraction improves FD-fNIRS BCI

Robin Dale; Hamid Dehghani

doi:10.1117/12.2650067

7 March 2023 System-driven convolutional feature extraction improves FD-fNIRS BCI

Robin Dale, Hamid Dehghani

Proceedings Volume PC12376, Optical Tomography and Spectroscopy of Tissue XV; PC123760A (2023) https://doi.org/10.1117/12.2650067
Event: SPIE BiOS, 2023, San Francisco, California, United States

Abstract

An intuitive and generalisable approach to spatial-temporal feature extraction for brain-computer interface (BCI) with high-density functional near-infrared spectroscopy (fNIRS) data is proposed, demonstrated here with frequency-domain (FD) signals for motor-task classification. Statistical analysis of the results shows that the spatially resolved convolutional neural network (CNN) model improves classification accuracy by 2.5% compared to a standard temporal CNN, further enhanced by data availability. This is a significant improvement considering the requirements of real-time BCI.

Conference Presentation

Citation Download Citation

Robin Dale and Hamid Dehghani "System-driven convolutional feature extraction improves FD-fNIRS BCI ", Proc. SPIE PC12376, Optical Tomography and Spectroscopy of Tissue XV, PC123760A (7 March 2023); https://doi.org/10.1117/12.2650067

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available