Liu, T.; Li, B.; Zhang, C.; Chen, P.; Zhao, W.; Yan, B. Real-Time Classification of Motor Imagery Using Dynamic Window-Level Granger Causality Analysis of fMRI Data. Brain Sci.2023, 13, 1406.
Liu, T.; Li, B.; Zhang, C.; Chen, P.; Zhao, W.; Yan, B. Real-Time Classification of Motor Imagery Using Dynamic Window-Level Granger Causality Analysis of fMRI Data. Brain Sci. 2023, 13, 1406.
Liu, T.; Li, B.; Zhang, C.; Chen, P.; Zhao, W.; Yan, B. Real-Time Classification of Motor Imagery Using Dynamic Window-Level Granger Causality Analysis of fMRI Data. Brain Sci.2023, 13, 1406.
Liu, T.; Li, B.; Zhang, C.; Chen, P.; Zhao, W.; Yan, B. Real-Time Classification of Motor Imagery Using Dynamic Window-Level Granger Causality Analysis of fMRI Data. Brain Sci. 2023, 13, 1406.
Abstract
This article presents a method for extracting neural signal features to identify the imagination of left and right hand grasping movements. A functional magnetic resonance imaging (fMRI) experiment is employed to identify four brain regions with significant activations during motor imagery(MI) and the effective connections between these regions of interest (ROIs) were calculated using Dynamic Window-level Granger Causality (DWGC). Then, a real-time fMRI(rt-fMRI) classification system for left and right hand MI is developed using the Open-NFT platform. The experimental results show that incorporating effective connections can enhance the average accuracy of real-time three-class classification (rest, left hand and right hand) by 3% in comparison to traditional multivoxel pattern classification analysis(MVPA). Moreover, it significantly improves classification accuracy during the initial stage of MI tasks while reducing the latency effects in real-time decoding. The study suggests that the effective connections obtained through the DWGC method serve as valuable features for real-time decoding of MI using fMRI. Moreover, they exhibit higher sensitivity to changes in brain states. This research offers theoretical support and technical guidance for extracting neural signal features in the context of fMRI-based studies.
Keywords
BCI, rt-fMRI, MI, DWGC, svm
Subject
Biology and Life Sciences, Neuroscience and Neurology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
