2018 International Joint Conference on Neural Networks (IJCNN) 2018
DOI: 10.1109/ijcnn.2018.8489503
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Cited by 4 publications
(2 citation statements)
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“…Moreover, great effort was put in the last decade by the ILAE in standardising the equipment, recording and storage of EEG data 10 21. Decades of research have suggested that the automated analysis of EEG can identify hidden differences between with epilepsy and non-epileptic subjects in terms of connectivity,22–24 signal predictability and complexity,25 26 spectral power27 28 and chaoticity 29. Computational analysis of EEG holds the promise of extracting information that is invisible to the naked eye of the human interpreter, in an objective and reproducible manner.…”
Section: Introductionmentioning
confidence: 99%
“…Moreover, great effort was put in the last decade by the ILAE in standardising the equipment, recording and storage of EEG data 10 21. Decades of research have suggested that the automated analysis of EEG can identify hidden differences between with epilepsy and non-epileptic subjects in terms of connectivity,22–24 signal predictability and complexity,25 26 spectral power27 28 and chaoticity 29. Computational analysis of EEG holds the promise of extracting information that is invisible to the naked eye of the human interpreter, in an objective and reproducible manner.…”
Section: Introductionmentioning
confidence: 99%
“…Moreover, great effort was put in the last decade by the ILAE in standardizing the equipment, recording and storage of EEG data 10,21 . Decades of research have demonstrated that the automated analysis of EEG can identify hidden differences between with epilepsy and non-epileptic subjects in terms of connectivity [22][23][24] , signal predictability and complexity 25,26 , spectral power 27,28 , and chaoticity 29 . Computational analysis of EEG holds the promise of extracting information that is invisible to the naked eye of the human interpreter, in an objective and reproducible manner.…”
Section: Introductionmentioning
confidence: 99%