TY - JOUR
T1 - ER-detect
T2 - a pipeline for robust detection of early evoked responses in BIDS-iEEG electrical stimulation data
AU - van den Boom, Max A
AU - Gregg, Nicholas M
AU - Valencia, Gabriela Ojeda
AU - Lundstrom, Brian N
AU - Miller, Kai J
AU - van Blooijs, Dorien
AU - Huiskamp, Geertjan J M
AU - Leijten, Frans S S
AU - Worrell, Gregory A
AU - Hermes, Dora
N1 - Publisher Copyright:
© 2025 Elsevier B.V.
PY - 2025/6
Y1 - 2025/6
N2 - BACKGROUND: Human 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. However, the methods used for the detection of responses in cortico-cortical evoked potential (CCEP) data vary across studies, from visual inspection with manual annotation to a variety of automated methods.NEW METHOD: To provide a robust workflow to process CCEP data and detect early evoked responses in a fully automated and reproducible fashion, we developed the Early Response (ER)-detect toolbox. 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 early response detection methods, which were validated against 14 manually annotated CCEP datasets from two different clinical sites by four independent raters.RESULTS: and comparison with existing methods: 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.CONCLUSION: 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 for both research and clinical purposes.
AB - BACKGROUND: Human 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. However, the methods used for the detection of responses in cortico-cortical evoked potential (CCEP) data vary across studies, from visual inspection with manual annotation to a variety of automated methods.NEW METHOD: To provide a robust workflow to process CCEP data and detect early evoked responses in a fully automated and reproducible fashion, we developed the Early Response (ER)-detect toolbox. 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 early response detection methods, which were validated against 14 manually annotated CCEP datasets from two different clinical sites by four independent raters.RESULTS: and comparison with existing methods: 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.CONCLUSION: 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 for both research and clinical purposes.
KW - Automated detection toolbox
KW - BIDS
KW - Cortico-cortical evoked potential (CCEP)
KW - Early evoked responses
KW - Intracranial EEG (iEEG)
KW - N1
UR - http://www.scopus.com/inward/record.url?scp=86000559749&partnerID=8YFLogxK
U2 - 10.1016/j.jneumeth.2025.110389
DO - 10.1016/j.jneumeth.2025.110389
M3 - Article
C2 - 39952481
SN - 0165-0270
VL - 418
JO - Journal of Neuroscience Methods
JF - Journal of Neuroscience Methods
M1 - 110389
ER -