Combining robust level extraction and unsupervised adaptive classification for high-accuracy fNIRS-BCI: An evidence on single-trial differentiation between mentally arithmetic- and singing-tasks

Combining robust level extraction and unsupervised adaptive classification for high-accuracy fNIRS-BCI: An evidence on single-trial differentiation between mentally arithmetic- and singing-tasks

Functional near-infrared spectroscopy (fNIRS) is a safe and non-invasive optical imaging technique that is being increasingly used in brain-computer interfaces (BCIs) to recognize mental tasks. Unlike electroencephalography (EEG) which directly measures neural activation, fNIRS signals reflect neuro...

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Bibliographic Details
Main Authors: Yao Zhang, Dongyuan Liu, Pengrui Zhang, Tieni Li, Zhiyong Li, Feng Gao
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-10-01
Series:Frontiers in Neuroscience
Subjects:
activation level
adaptive Gaussian mixture model
brain-computer interface
classification accuracy
fNIRS
feature extraction
Online Access:https://www.frontiersin.org/articles/10.3389/fnins.2022.938518/full

Internet

https://www.frontiersin.org/articles/10.3389/fnins.2022.938518/full

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