Project ID: R01NS112497

Investigators: Nuri Ince

Dataset Size: 86.93 GB

Files: 187

Description:

Prolonged iEEG monitoring for SOZ localization does add to the risk of complications and may include serious issues, such as intracranial bleeding, meningoencephalitis, and eventually death. The intellectual merit of this project is to develop computational intelligence tools based on recent advances in sparse coding and unsupervised machine learning techniques to investigate stereotyped high frequency oscillations (HFOs) in long-term iEEG and test the hypothesis whether the automated detection of HFOs will yield accurate and fast identification of SOZ.

For questions about this dataset, contact ince.nuri@mayo.edu

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