Number of EEG Signal Components Estimated Using the Short-Term Renyi Entropy
| Autor: | Jonatan Lerga, Rebeka Lerga, Vladimir Mozetič, Nicoletta Saulig |
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| Předmět: |
Signal processing
Quantitative Biology::Neurons and Cognition medicine.diagnostic_test business.industry Brain activity and meditation Physics::Medical Physics Spectral density Pattern recognition Electroencephalography Short-term R´enyi entropy Multi-component signals Time-frequency signal analysis Time–frequency analysis Rényi entropy medicine Spectrogram Artificial intelligence Entropy (energy dispersal) business Mathematics |
| Zdroj: | ResearcherID |
| Popis: | Multichannel electroencephalogram (EEG) signals are known to be highly non-stationary and often multi-component. A new method for its complexity, in terms of number of signal components extracted from its time-frequency distributions, has been proposed in this paper. Exploiting its spectral energy variation with time, the joint time-frequency distribution approach was upgraded by the modification of Renyi entropy, called short-term Renyi entropy, and applied to multichannel EEG signal analysis resulting in novel algorithm for its complexity detection. Number of EEG signals components obtained for various EEG signals was shown to provide useful information concerning brain activity at each electrode location, which may further be used to detect the brain activity abnormalities for patients with limb movement difficulties. |
| Databáze: | OpenAIRE |
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