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

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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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Data availability

The data that support the findings of this study are openly available on the PhysioNet website (https://physionet.org).

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (#62006197, #42305067), the project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (#SML2023SP203), Medical Science and Technology Research Fund of Guangdong Province (B2023186).

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Authors and Affiliations

  1. Mental Health Education Center and School of Science, Xihua University, Chengdu, 610039, China

    Yunhong Liu, Shiqi Yu, Jia Li, Shan Sun & Tao Zhang

  2. The Artificial Intelligence Group, Division of Frontier Research, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519000, China

    Jiwang Ma & Tao Zhang

  3. School of Computer and Software, Chengdu Jincheng College, Chengdu, 610097, China

    Fei Wang

  4. MOE Key Laboratory for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Dezhong Yao, Peng Xu & Tao Zhang

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  1. Yunhong Liu
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Contributions

YL: methodology, formal analysis, visualization, and writing. SY: data preprocessing and formal analysis. JL, JM, FW, and SS: data curation. DY and PX: Writing—review & editing. TZ: Funding acquisition, idea, Writing—review & editing.

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Liu, Y., Yu, S., Li, J. et al. Brain state and dynamic transition patterns of motor imagery revealed by the bayes hidden markov model. Cogn Neurodyn (2024). https://doi.org/10.1007/s11571-024-10099-9

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