The brain state of motor imagery is reflected in the causal information of functional near-infrared spectroscopy

Abstract

Background Brain-computer interface (BCI) is a promising neurorehabilitation strategy for ameliorating post-stroke function disorders. Physiological changes in the brain, such as functional near-infrared spectroscopy (fNIRS) dedicated to exploring cerebral circulatory responses during neurological rehabilitation tasks, are essential for gaining insights into neurorehabilitation mechanisms. However, the relationship between the neurovascular responses in different brain regions under rehabilitation tasks remains unknown.Objective The present study explores the fNIRS interactions between brain regions under different motor imagery (MI) tasks, emphasizing functional characteristics of brain network patterns and BCI motor task classification.Methods Granger causality analysis (GCA) is carried out for oxyhemoglobin data from 29 study participants in left-and right-hand MI tasks. ResultsAccording to research findings, homozygous and heterozygous states in the two brain connectivity modes reveal one and nine channel pairs, respectively, with significantly different (P < 0.05) GC values under the left-and right-hand MI tasks in the population. With reference to the total 10 channel pairs of causality differences between the two brain working states, a support vector machine is used to classify the two tasks with an overall accuracy of 83% for five-fold cross-validation. ConclusionAs demonstrated in the present study, fNIRS offers causality patterns in different brain states of MIBCI motor tasks. The research findings show that fNIRS causality can be used to assess different states of the brain, providing theoretical support for its application to neurorehabilitation assessment protocols to ultimately improve patients' quality of life.

Vibrotactile enhancement in hand rehabilitation has a reinforcing effect on sensorimotor brain activities

“…The GC test assumes that these variable time series include all the predictive information for both y and x . The following is an explicit description of GC using an autoregressive process, and the model order was identified using the Bayesian information criterion ( Duggento et al, 2018 ; Du et al, 2022 ). The Durbin Watson Test was used to measure the autocorrelation of residuals in the regression analysis ( Durbin and Watson, 1950 ; Duggento et al, 2018 ).…”