Normalized mutual information of fNIRS signals as a measure for accessing typical and atypical brain activity.
Sign Up to like & getrecommendations! Published in 2023 at "Journal of biophotonics"
DOI: 10.1002/jbio.202200369
Abstract: Normalized mutual information (NMI) can be used to detect statistical correlations between time series. We showed possibility of using NMI to quantify synchronicity of information transmission in different brain regions, thus to characterize functional connections,… read more here.
Keywords: information; mutual information; normalized mutual; brain activity ... See more keywords
Effective Brain Connectivity for fNIRS With Fuzzy Cognitive Maps in Neuroergonomics
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Cognitive and Developmental Systems"
DOI: 10.1109/tcds.2019.2958423
Abstract: Effective connectivity (EC) among functional near-infrared spectroscopy (fNIRS) signals is a quantitative measure of the strength of influence between brain activity associated with different regions of the brain. Evidently, accurate deciphering of EC gives further… read more here.
Keywords: connectivity; fuzzy cognitive; methodology; brain ... See more keywords
Hybrid EEG-fNIRS Brain Computer Interface Based on Common Spatial Pattern by Using EEG-Informed General Linear Model
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Instrumentation and Measurement"
DOI: 10.1109/tim.2023.3276509
Abstract: Hybrid brain–computer interfaces (BCI) utilizing the high temporal resolution of electroencephalography (EEG) and the high spatial resolution of functional near-infrared spectroscopy (fNIRS) are preferred over single-modal BCIs. However, due to the large dimensionality of the… read more here.
Keywords: general linear; brain computer; eeg fnirs; eeg ... See more keywords
Explainable artificial intelligence model to predict brain states from fNIRS signals
Sign Up to like & getrecommendations! Published in 2023 at "Frontiers in Human Neuroscience"
DOI: 10.3389/fnhum.2022.1029784
Abstract: Objective: Most Deep Learning (DL) methods for the classification of functional Near-Infrared Spectroscopy (fNIRS) signals do so without explaining which features contribute to the classification of a task or imagery. An explainable artificial intelligence (xAI)… read more here.
Keywords: explainable artificial; classification; brain states; fnirs signals ... See more keywords