Subject-Independent Functional Near-Infrared Spectroscopy-Based Brain–Computer Interfaces Based on Convolutional Neural Networks

• Published in: Frontiers in human neuroscience Vol. 15; p. 646915
• Main Authors: Kwon, Jinuk, Im, Chang-Hwan
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 12.03.2021; Frontiers Media S.A
• Subjects: Accuracy; Attention; binary communication; brain–computer interface; Calibration; Classification; convolutional neural network; Datasets; Deep learning; Discriminant analysis; Electroencephalography; functional near-infrared spectroscopy; Human Neuroscience; I.R. radiation; Infrared spectroscopy; Interfaces; Mathematics; Mental task performance; Neural networks; Performance evaluation; Spectrum analysis; deep learning; brain–computer interface; convolutional neural network; functional near-infrared spectroscopy; binary communication

Functional near-infrared spectroscopy (fNIRS) has attracted increasing attention in the field of brain–computer interfaces (BCIs) owing to their advantages such as non-invasiveness, user safety, affordability, and portability. However, fNIRS signals are highly subject-specific and have low test-rete...