scholarly journals Resting state functional connectivity of the whole head with near-infrared spectroscopy

2010 ◽  
Vol 1 (1) ◽  
pp. 324 ◽  
Author(s):  
Rickson C. Mesquita ◽  
Maria A. Franceschini ◽  
David A. Boas
Keyword(s):  
Infrared Spectroscopy ◽  
Functional Connectivity ◽  
Near Infrared Spectroscopy ◽  
Resting State ◽  
Near Infrared ◽  
Resting State Functional Connectivity

Performance Prediction for a Near-Infrared Spectroscopy-Brain–Computer Interface Using Resting-State Functional Connectivity of the Prefrontal Cortex

2018 ◽  
Vol 28 (10) ◽  
pp. 1850023 ◽  
Author(s):  
Jaeyoung Shin ◽  
Chang-Hwan Im
Keyword(s):  
Prefrontal Cortex ◽  
Infrared Spectroscopy ◽  
Functional Connectivity ◽  
Near Infrared Spectroscopy ◽  
Resting State ◽  
Near Infrared ◽  
Mental Arithmetic ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Resting State Functional Connectivity

One of the most important issues in current brain–computer interface (BCI) research is the prediction of a user’s BCI performance prior to the main BCI session because it would be useful to reduce the time required to determine the BCI paradigm best suited to that user. In electroencephalography (EEG)-BCI research, whether a user has low BCI performance toward a specific BCI paradigm has been estimated using a variety of resting-state EEG features. However, no previous study has attempted to predict the performance of near-infrared spectroscopy (NIRS)-BCI using resting-state NIRS data recorded before the main BCI experiment. In this study, we investigated whether the performance of an NIRS-BCI discriminating a mental arithmetic task from the baseline state could be predicted using resting-state functional connectivity (RSFC) of the prefrontal cortex. The investigation of NIRS signals recorded from 29 participants revealed that the RSFC between bilateral channels in the prefrontal area was negatively correlated with subsequent BCI performance (e.g. a fitted line for the RSFC between L2 and R2 channels explains 41% of BCI performance variation). We expect that our indicator can be used to predict BCI performance of an individual user prior to the main NIRS-BCI experiments, thereby facilitating implementation of more efficient NIRS-BCI systems.

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