Assessing expert reliability in determining intracranial EEG channel quality and introducing the automated bad channel detection algorithm.

ABSTRACT: Objective.To evaluate the inter- and intra-rater reliability for the identification of bad channels among neurologists, EEG Technologists, and naïve research personnel, and to compare their performance with the automated bad channel detection (ABCD) algorithm for detecting bad channels.Approach.Six Neurologists, ten EEG Technologists, and six naïve research personnel (22 raters in total) were asked to rate 1440 real intracranial EEG channels as good or bad. Intra- and interrater kappa statistics were calculated for each group. We then compared each group to the ABCD algorithm which uses spectral and temporal domain features to classify channels as good or bad.Main results.Analysis of channel ratings from our participants revealed variable intra-rater reliability within

SUBMITTER: Hattab T 

PROVIDER: S-EPMC12320219 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature