Estimation of Effective Fronto-Parietal connectivity during Motor Imagery using partial granger causality analysis

Abstract

Connectivity analysis has become an essential tool for the evaluation of functional brain dynamics. The functional connectivity between different parts of the brain, or between different sensors, is assumed to provide key information for the discrimination of brain responses. In this study, we propose an estimation of effective cortical connectivity measures in frontal and parietal areas of human brain during four different Motor Imagery (MI) tasks. Feedback based brain-computer interface (BCI) technology has been successfully implemented for recovery of stroke patients as it can enhance the neural plasticity in brain areas associated with motor execution. However, it is still challenging to obtain reliable information regarding improvement in neural functioning during rehabilitation and its neuro-physiological dynamics. Brain connectivity is a reliable biomarker associated with brain functionality. Here, we evaluate to what extent partial granger causality can provide information in form of effective neural connectivity that can differentiate motor imagery tasks. Our results on nine subjects using the EEG dataset (BCI competition 2008 dataset 2A) show distinct connectivity patterns for all four MI classes, and higher information flow in the fronto-parietal network during task phase as compared to non-task phase. The results support the conclusion that effective connectivity analysis through partial granger causality can provide key information about neural interactions specific to different MI tasks. Moreover these interactions can be utilized as reliable biomarkers for assessment of motor recovery during stroke rehabilitation.

On the Assessment of Functional Connectivity in an Immersive Brain-Computer Interface During Motor Imagery

“…Our results show the frontal and parietal brain regions as target areas during 3PP, mainly in β. This is consistent with Rathee et al (2016) who reported that, using the partial Granger causality during the right hand MI, the higher FIGURE 13 | Mean node betweenness centrality for both participants shown in color only at the sensors with an incremental trend in comparison to the other condition. For each frequency band, the heads to the left (A,C,E) show the cases when the node betweenness centrality in 1PP increased in comparison to 3PP, while the heads to the right (B,D,F) show those for which 3PP increased in comparison to 1PP.…”

A Parametric Time-Frequency Conditional Granger Causality Method Using Ultra-Regularized Orthogonal Least Squares and Multiwavelets for Dynamic Connectivity Analysis in EEGs

“…[4][5] show that, for left-and right-hand MI tasks, the electrode signals recorded from opposite position function mainly as the causal source while the ipsilateral channel signals operate mostly as the net target almost within the whole 2 s after stimulus. These alpha oscillatory activities at scalp-level are in agreement with the direction of information flow postulated in the MI literatures [5,40].…”

Single-trial effective brain connectivity patterns enhance discriminability of mental imagery tasks

“…In the present work, in line with our previous study [23], we estimate the single-trial directed functional connectivity features to elucidate the interaction among the EEG signals during various MI tasks (left hand, right hand, feet, and tongue) and CI tasks (word generation, spatial navigation, and subtraction imagery task). In particular, we implemented time-domain partial granger causality (PGC) [24] along with PDC and DTF on the two publically available datasets.…”