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27-03-2024 | Research Article

Brain state and dynamic transition patterns of motor imagery revealed by the bayes hidden markov model

Authors: Yunhong Liu, Shiqi Yu, Jia Li, Jiwang Ma, Fei Wang, Shan Sun, Dezhong Yao, Peng Xu, Tao Zhang

Published in: Cognitive Neurodynamics

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Abstract

Motor imagery (MI) is a high-level cognitive process that has been widely applied to brain-computer inference (BCI) and motor recovery. In practical applications, however, huge individual differences and unclear neural mechanisms have seriously hindered the application of MI and BCI systems. Thus, it is urgently needed to explore MI from a new perspective. Here, we applied a hidden Markov model (HMM) to explore the dynamic organization patterns of left- and right-hand MI tasks. Eleven distinct HMM states were identified based on MI-related EEG data. We found that these states can be divided into three metastates by clustering analysis, showing a highly organized structure. We also assessed the probability activation of each HMM state across time. The results showed that the state probability activation of task-evoked have similar trends to that of event-related desynchronization/synchronization (ERD/ERS). By comparing the differences in temporal features of HMM states between left- and right-hand MI, we found notable variations in fractional occupancy, mean life time, mean interval time, and transition probability matrix across stages and states. Interestingly, we found that HMM states activated in the left occipital lobe had higher occupancy during the left-hand MI task, and conversely, during the right-hand MI task, HMM states activated in the right occipital lobe had higher occupancy. Moreover, significant correlations were observed between BCI performance and features of HMM states. Taken together, our findings explored dynamic networks underlying the MI-related process and provided a complementary understanding of different MI tasks, which may contribute to improving the MI-BCI systems.

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Appendix
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Metadata
Title
Brain state and dynamic transition patterns of motor imagery revealed by the bayes hidden markov model
Authors
Yunhong Liu
Shiqi Yu
Jia Li
Jiwang Ma
Fei Wang
Shan Sun
Dezhong Yao
Peng Xu
Tao Zhang
Publication date
27-03-2024
Publisher
Springer Netherlands
Published in
Cognitive Neurodynamics
Print ISSN: 1871-4080
Electronic ISSN: 1871-4099
DOI
https://doi.org/10.1007/s11571-024-10099-9