Deep learning for epileptogenic zone delineation from the invasive EEG: challenges and lookouts

Sem Hoogteijling; Maeike Zijlmans

doi:10.1093/braincomms/fcab307

Deep learning for epileptogenic zone delineation from the invasive EEG: challenges and lookouts

Sem Hoogteijling^*
, Maeike Zijlmans

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

1 Citation (Scopus)

3 Downloads (Pure)

Abstract

This scientific commentary refers to 'Refining epileptogenic high-frequency oscillations using deep learning: a reverse engineering approach' by Zhang et al. (https://doi.org/10.1093/braincomms/fcab267).

Original language	English
Article number	fcab307
Pages (from-to)	1-4
Journal	Brain communications
Volume	4
Issue number	1
DOIs	https://doi.org/10.1093/braincomms/fcab307
Publication status	Published - 2022

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10.1093/braincomms/fcab307Licence: CC BY

fcab307Final published version, 419 KBLicence: CC BY

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title = "Deep learning for epileptogenic zone delineation from the invasive EEG: challenges and lookouts",

abstract = "This scientific commentary refers to 'Refining epileptogenic high-frequency oscillations using deep learning: a reverse engineering approach' by Zhang et al. (https://doi.org/10.1093/braincomms/fcab267).",

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N2 - This scientific commentary refers to 'Refining epileptogenic high-frequency oscillations using deep learning: a reverse engineering approach' by Zhang et al. (https://doi.org/10.1093/braincomms/fcab267).

AB - This scientific commentary refers to 'Refining epileptogenic high-frequency oscillations using deep learning: a reverse engineering approach' by Zhang et al. (https://doi.org/10.1093/braincomms/fcab267).

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DO - 10.1093/braincomms/fcab307

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Deep learning for epileptogenic zone delineation from the invasive EEG: challenges and lookouts

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