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Updated: May 22, 2026

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
Published on: December 18, 2016
EEG microstate analysis in temporal lobe epilepsy: a comparative study stratified by surgical outcome
Huanfa Li1, Lishuo Liu2, Qiang Meng1
1Department of Neurosurgery, Comprehensive Epilepsy Center, The First Affiliated Hospital of Xi'an JiaoTong University, Xi'an 710061, China; Clinical Research Center for Refractory Epilepsy of Shaanxi Province, Xi'an 710061, China.
Objective:
This study investigated electroencephalogram (EEG) microstate characteristics in patients with temporal lobe epilepsy (TLE) following functional anterior temporal lobectomy (FATL), aiming to identify postoperative alterations in microstate dynamics specifically associated with seizure-free outcomes and to characterize baseline microstate features that may assist in the selection of optimal candidates for TLE surgery.
Methods:
A total of 44 TLE patients were included in the study, comprising 27 seizure-free (SF) and 17 non-seizure-free (nSF) individuals. For each patient, a 2 min EEG data segment was selected for microstate analysis. Microstate characteristics-including mean duration, time coverage, frequency of occurrence, and transition probability-were compared pre- and postoperatively within each group. Baseline microstate features were also compared between groups prior to surgery.
Results:
Following surgery, the SF group showed a significant, specific increase in the time coverage of microstate B and a significant decrease in the time coverage of microstate D. The bidirectional transition probabilities between microstates A and B showed marked increases, whereas those between microstates C and D showed significant decreases. At baseline prior to surgery, no statistically significant differences were observed between the SF and nSF groups with respect to any of the microstate parameters.
Conclusions:
The specific alterations in postoperative microstate dynamics observed in TLE patients support the feasibility of predicting long-term postoperative outcomes using a brief postoperative EEG monitoring.

