Published in the Journal of Neural Engineering, a research team led by the University of Minnesota Medical School has evaluated the reliability of human experts in comparison to an automated algorithm in assessing the quality of intracranial electroencephalography (iEEG) data. This research hopes to pave the way for more accurate and efficient seizure detection and localization, ultimately improving outcomes for epilepsy patients.
Novel algorithm improves intracranial EEG accuracy to enhance future patient care
