Exploring spatiotemporal patterns of high-dimensional electroencephalography (EEG) time series generated from complex brain system is crucial for deciphering aging and cognitive functioning. Analyzing high-dimensional EEG series poses challenges, particularly when employing distance-based methods for spatiotemporal dynamics. Therefore, we proposed an innovative methodology for multi-channel EEG data, termed as Spatiotemporal Information-based Similarity (STIBS) analysis. The core of this method is to first perform state space compression of multi-channel EEG time series using global field power, which can provide insight into the dynamic integration of spatiotemporal patterns between the steady states and non-steady states of brain. Subsequently, we quantify the pairwise differences and non-randomness of spatiotemporal patterns using an information-based similarity analysis. Results demonstrated that this method holds the potential to serve as a distinguishing marker between young and elderly on both pairwise differences and non-randomness indices. Young individuals and those with higher cognitive abilities exhibit more complex macrostructure and non-random spatiotemporal patterns, whereas both aging and cognitive decline lead to more randomized spatiotemporal patterns. We further extended the proposed analytics to brain regions adversarial STIBS (bra-STIBS), highlighting differences between young and elderly, as well as high and low cognitive groups. Furthermore, utilizing the STIBS-based XGBoost model yields superior recognition accuracy in aging (93.05%) and cognitive functioning (74.29%, 64.19%, and 80.28%, respectively, for attention, memory, and compatibility performance recognition). STIBS-based methodology not only contributes to the ongoing exploration of neurobiological changes in aging but also provides a powerful tool for characterizing the spatiotemporal nonlinear dynamics of the brain and their implications for cognitive functioning.
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November 2024
Research Article|
November 08 2024
Decoding aging and cognitive functioning through spatiotemporal EEG patterns: Introducing spatiotemporal information-based similarity analysis
Wang Wan;
Wang Wan
(Data curation, Formal analysis, Methodology, Writing – original draft)
1
State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University
, Nanjing 210096, China
2
Center for Nonlinear Dynamics in Medicine, Southeast University
, Nanjing 210096, China
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Zhilin Gao;
Zhilin Gao
(Formal analysis, Investigation)
1
State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University
, Nanjing 210096, China
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Zhongze Gu;
Zhongze Gu
(Supervision)
1
State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University
, Nanjing 210096, China
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Chung-Kang Peng
;
Chung-Kang Peng
(Writing – review & editing)
2
Center for Nonlinear Dynamics in Medicine, Southeast University
, Nanjing 210096, China
3
Key Laboratory of Child Development and Learning Science, Ministry of Education, School of Biological Science and Medical Engineering, Southeast University
, Nanjing 210096, China
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Xingran Cui
Xingran Cui
a)
(Funding acquisition, Supervision, Validation, Writing – review & editing)
2
Center for Nonlinear Dynamics in Medicine, Southeast University
, Nanjing 210096, China
3
Key Laboratory of Child Development and Learning Science, Ministry of Education, School of Biological Science and Medical Engineering, Southeast University
, Nanjing 210096, China
a)Author to whom correspondence should be addressed: cuixr@seu.edu.cn
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a)Author to whom correspondence should be addressed: cuixr@seu.edu.cn
Chaos 34, 113124 (2024)
Article history
Received:
February 11 2024
Accepted:
October 21 2024
Citation
Wang Wan, Zhilin Gao, Zhongze Gu, Chung-Kang Peng, Xingran Cui; Decoding aging and cognitive functioning through spatiotemporal EEG patterns: Introducing spatiotemporal information-based similarity analysis. Chaos 1 November 2024; 34 (11): 113124. https://doi.org/10.1063/5.0203249
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