Machine learning for (non-)epileptic tissue detection from the intraoperative electrocorticogram.

Objective: Clinical visual intraoperative electrocorticography (ioECoG) reading intends to localize epileptic tissue and improve epilepsy surgery outcome. We aimed to understand whether machine learning (ML) could complement ioECoG reading, how subgroups affected performance, and which ioECoG features were most important.

Methods: We included 91 ioECoG-guided epilepsy surgery patients with Engel 1A outcome.

Prognostic Value of Complete Resection of the High-Frequency Oscillation Area in Intracranial EEG: A Systematic Review and Meta-Analysis.

Background And Objectives: High-frequency oscillations (HFOs; ripples 80-250 Hz; fast ripples [FRs] 250-500 Hz) recorded with intracranial electrodes generated excitement and debate about their potential to localize epileptogenic foci. We performed a systematic review and meta-analysis on the prognostic value of complete resection of the HFOs-area (crHFOs-area) for epilepsy surgical outcome in intracranial EEG (iEEG) accessing multiple subgroups.

Methods: We searched PubMed, Embase, and Web of Science for original research from inception to October 27, 2022.

Deep learning for epileptogenic zone delineation from the invasive EEG: challenges and lookouts.

This scientific commentary refers to 'Refining epileptogenic high-frequency oscillations using deep learning: a reverse engineering approach' by Zhang . (https://doi.org/10.