Abstract
Electroencephalogram (EEG) recordings contain a large amount of information about physiological and pathological processes in the brain and serve as one of the important tools in clinical diagnosis and research regarding epilepsy. Dynamical similarity analysis is applied to characterize EEG changes in different absence seizure states. The average similarity measure of a pair of EEG signals in the same seizure states and across different seizure states is calculated using an improved dynamical similarity method. The results show that the average similarity measures between EEG segments within the seizure-free state are close to 1, suggesting that the EEG segments within the seizure-free state share the same dynamic characteristics. The similarity measures between EEG segments across different seizure states are typically smaller, indicating that the changes of dynamic characteristics can be found during different absence seizure states.
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Ouyang, G., Li, X. (2016). Dynamical Similarity Analysis of EEG Recordings. In: Li, X. (eds) Signal Processing in Neuroscience. Springer, Singapore. https://doi.org/10.1007/978-981-10-1822-0_7
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DOI: https://doi.org/10.1007/978-981-10-1822-0_7
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