Unsupervised machine-learning classification of electrophysiologically active electrodes during human cognitive task performance.

ABSTRACT: Identification of active electrodes that record task-relevant neurophysiological activity is needed for clinical and industrial applications as well as for investigating brain functions. We developed an unsupervised, fully automated approach to classify active electrodes showing event-related intracranial EEG (iEEG) responses from 115 patients performing a free recall verbal memory task. Our approach employed new interpretable metrics that quantify spectral characteristics of the normalized iEEG signal based on power-in-band and synchrony measures. Unsupervised clustering of the metrics identified distinct sets of active electrodes across different subjects. In the total population of 11,869 electrodes, our method achieved 97% sensitivity and 92.9% specificity with the most efficient metri

SUBMITTER: Saboo KV 

PROVIDER: S-EPMC6874617 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature