Abstract
Long-term exposure to high altitude, low pressure and low oxygen will seriously threaten people’s cognitive function. To explore the changes in whole-brain network dynamics during brain activity in long-term high-altitude migrants, EEG signals from three subjects of 75 different altitudes were analyzed using the Stroop experimental paradigm and the network recombination prediction model. The sliding window method was used to explore the dynamic change process of the brain network. At the same time, the time period with significant difference between the brain networks of the altitude group was selected as the real response network to measure the model prediction accuracy. Then, according to different network prediction model rules, the weights of brain network 200 ms before stimulation were updated for each subject. Finally, the prediction model with the least difference between the prediction network and the real response network was selected for each subject. The experimental results showed that the prediction accuracy of the model reach 98.95%, and there is a significant difference in model selection between the elevation groups. It helps to understand the brain dynamics of healthy people, and reveals the abnormal changes in the brain networks of those who have stayed at high altitude for a long time, providing an important reference for the cognitive rehabilitation training of victims ex-posed at high altitude.
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Acknowledgments
This work was supported by the Next Generation Internet Technology Innovation Project of Celtic Network (No. NGII20181206), the National Natural Science Foundation of China (No. 61976150), the Key R & D Projects of Shanxi Province (No. 201803D31038).
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Ma, Y., Wang, L., Yang, Y., Li, X., Xu, Z., Li, H. (2021). Modeling and Analysis of EEG Brain Network in High Altitude Task State. In: Zeng, J., Qin, P., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2021. Communications in Computer and Information Science, vol 1451. Springer, Singapore. https://doi.org/10.1007/978-981-16-5940-9_36
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DOI: https://doi.org/10.1007/978-981-16-5940-9_36
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