Analysis of MI Brain Causal Networks Under Different Visual Stimuli Guidance
Bian Yan1,2, Zhao Li2, Fu Xing1, Qi Hongzhi1*
1(School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China) 2(Tianjin Information Sensing & Intelligent Control Key Lab, Tianjin University of Technology and Education, Tianjin 300222, China)
Abstract Brain-computer interface (BCI) based on motor imagery (MI) is believed to be a potential approach for motor rehabilitation. However, classical MI-BCI leaves the questions of large individual differences and low recognition accuracy. By making use of visual stimuli guidance could enhance MI features and improve BCI recognition accuracy. Nevertheless, rare attention has been paid to the features of brain causal networks during MI under different visual stimuli paradigms and their impacts on motor recovery. We designed four different types of visual stimuli guidance in this paper, including dynamic/non-dynamic stimuli and simple/complex MI tasks. The beta one - tailed one - sample t test (P<0.01) causal significant connectivity networks of seven regions of interest located in motor-sensory cortex were built based on isolated effective coherence (iCoh) during MI, and parameters of degree distribution, clustering coefficient, global efficiency and betweenness centrality were further analyzed. The outcomes showed that compared with non-dynamic and simple MI task experimental paradigm, the average degree distribution of dynamic visual paradigms combined with complex MI task was varied from 2.143 to 2.429; the clustering coefficient was varied from 0.643 to 0.767; the global efficienciewas varied from 0.393 to 0.417. Under dynamic with complex task paradigms, the significant connectivity exist between SMA and SPL, IPL, thus SMA becomes the key node in the brain causal networks.
Bian Yan,Zhao Li,Fu Xing, et al. Analysis of MI Brain Causal Networks Under Different Visual Stimuli Guidance[J]. Chinese Journal of Biomedical Engineering, 2020, 39(6): 685-692.
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