Abstract
A long-standing issue in the field of neuroscience is identifying evolving patterns from multivariate electroencephalography (EEG) signals superimposed with intensive noise. With insufficient prior knowledge, it becomes even more important to (1) accurately detect synchronization dynamics among data channels and (2) adaptively classify evolving patterns to better characterize the intrinsic nature of brain activities represented by the EEG. This study uses a shadow-dense network approach to solve these problems. The maximal information coefficient (MIC) method is extended to enable global synchronization measurement of all data channels embedded in the EEG. The global MIC measures are organized in time sequence to represent the evolving synchronization patterns. A shallow-dense neural network is designed to adaptively characterize the nonstationary patterns and then classify them. Experiments are performed to evaluate this approach over an epileptic EEG dataset. It is found that this approach can classify seizure states with accuracy, sensitivity, and specificity of \(97.292\%\), \(98.696\%\), and \(96.116\%\), respectively; these results are superior to those of most existing methods. The proposed approach achieves this performance without denoising the EEG; in contrast, denoising is essential in existing methods. Furthermore, the proposed approach requires only one hyperparameter, which avoids the potential errors caused by excessive parameter settings in existing methods.
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Ke, H. et al. (2018). A Shallow-Dense Network Approach to Synchronization Pattern Classification of Multivariate Epileptic EEG. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2017 Chinese Intelligent Systems Conference. CISC 2017. Lecture Notes in Electrical Engineering, vol 459. Springer, Singapore. https://doi.org/10.1007/978-981-10-6496-8_51
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DOI: https://doi.org/10.1007/978-981-10-6496-8_51
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