ER-detect: a pipeline for robust detection of early evoked responses in BIDS-iEEG electrical stimulation data

Author:

van den Boom Max A.ORCID,Gregg Nicholas M.ORCID,Valencia Gabriela OjedaORCID,Lundstrom Brian N.ORCID,Miller Kai J.ORCID,van Blooijs DorienORCID,Huiskamp Geertjan J.M.ORCID,Leijten Frans S.S.ORCID,Worrell Gregory A.ORCID,Hermes DoraORCID

Abstract

AbstractHuman brain connectivity can be measured in different ways. Intracranial EEG (iEEG) measurements during single pulse electrical stimulation provide a unique way to assess the spread of electrical information with millisecond precision. To provide a robust workflow to process these cortico-cortical evoked potential (CCEP) data and detect early evoked responses in a fully automated and reproducible fashion, we developed Early Response (ER)-detect. ER-detect is an open-source Python package and Docker application to preprocess BIDS structured iEEG data and detect early evoked CCEP responses. ER-detect can use three response detection methods, which were validated against 14 manually annotated CCEP datasets from two different sites by four independent raters. Results showed that ER-detect’s automated detection performed on par with the inter-rater reliability (Cohen’s Kappa of ∼0.6). Moreover, ER-detect was optimized for processing large CCEP datasets, to be used in conjunction with other connectomic investigations. ER-detect provides a highly efficient standardized workflow such that iEEG-BIDS data can be processed in a consistent manner and enhance the reproducibility of CCEP based connectivity results.

Publisher

Cold Spring Harbor Laboratory

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