Abstract
Estimating the functional interactions and connections between brain regions to corresponding process in cognitive, behavioral and psychiatric domains are central pursuits for understanding the human connectome. Few studies have examined the effects of dynamic evolution on cognitive processing and brain activation using wavelet coherence in scalp electroencephalography (EEG) data. Aim of this study was to investigate the brain functional connectivity and dynamic programming model based on the wavelet coherence from EEG data and to evaluate a possible correlation between the brain connectivity architecture and cognitive evolution processing. Here, We present an accelerated dynamic programing algorithm that we found that spatially distributed regions coherence connection difference, for variation audio stimulation, dynamic programing model give the dynamic evolution processing in difference time and frequency. Such methodologies will be suitable for capturing the dynamic evolution of the time varying connectivity patterns that reflect certain cognitive tasks or brain pathologies.
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Acknowledgement
Our thanks to supports from the National Natural Science Foundation of China (61171186, 61271345, 61671187), Foundamental Research Project of Shenzhen (JCYJ20150929143955341), Key Laboratory Opening Funding of MOE-Microsoft Key Laboratory of Natural Language Processing and Speech (HIT.KLOF.20150xx, HIT.KLOF.20160xx), and the Fundamental Research Funds for the Central Universities (HIT.NSRIF.2012047). The authors are grateful for the anonymous reviewers who made constructive comments.
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Fang, C., Li, H., Ma, L. (2016). EEG Brain Functional Connectivity Dynamic Evolution Model: A Study via Wavelet Coherence. In: Liu, CL., Hussain, A., Luo, B., Tan, K., Zeng, Y., Zhang, Z. (eds) Advances in Brain Inspired Cognitive Systems. BICS 2016. Lecture Notes in Computer Science(), vol 10023. Springer, Cham. https://doi.org/10.1007/978-3-319-49685-6_24
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DOI: https://doi.org/10.1007/978-3-319-49685-6_24
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