Abstract
In endeavor toward better understanding of brain functions, the analysis of information transfer between the various brain lobes plays a crucial role. Electroencephalogram (EEG) is an electrical brain signal in microvolts, which provides unique and important information about the brain dynamics. Frequency of EEG signal lies between 0 and 100 Hz. In epileptic or seizure related studies, decomposition of EEG signal into various frequency sub-bands such as α, β, \( \delta \), \( \theta \), and γ is essential. EEG plays a key role in diagnosis of neurological disorders such as epilepsy. In this paper, we explore decomposition of EEG by db18 wavelet, power spectral density, coherence, energy distribution, and empirical cumulative distribution function of EEGs. This work was carried out to study the changes in the normal and epileptic EEGs with respect to PSD, coherence, energy, and ECDF to check the suitability of these parameters as an input to the classifiers. The methodology was applied mainly to three groups consisting of male and females between the age group of 01–107 years: (1) healthy subjects (normal), (2) subjects with focal seizures, (3) subjects with generalized seizures. The work was carried out on the signals obtained from real subjects to study the EEG-based brain connectivity analysis. It was observed that PSD and coherence study related to the sub-bands reveal more accurate information than the study of complete EEG with or without the seizures.
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Shriram, R., Baskar, V.V., Martin, B., Sundhararajan, M., Daimiwal, N. (2019). Energy Distribution and Coherence-Based Changes in Normal and Epileptic Electroencephalogram. In: Satapathy, S., Bhateja, V., Das, S. (eds) Smart Intelligent Computing and Applications . Smart Innovation, Systems and Technologies, vol 104. Springer, Singapore. https://doi.org/10.1007/978-981-13-1921-1_61
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