Prediction of response to transcranial magnetic stimulation treatment for depression using EEG and machine learning

Authors

Neil W. Bailey (ORCID: https://orcid.org/0000-0002-8483-1068)
Hanneke van Dijk (ORCID: https://orcid.org/0000-0002-6692-3129)
Bernadette M. Fitzgibbon (ORCID: https://orcid.org/0000-0002-5339-5304)
Martijn Arns (ORCID: https://orcid.org/0000-0002-0610-7613)
Paul B. Fitzgerald (ORCID: https://orcid.org/0000-0003-4217-8096)
Ben Fulcher (ORCID: https://orcid.org/0000-0002-3003-4055)

Institutions

Australian National University (AU)
The University of Sydney (AU)
Maastricht University (NL)
Brainclinics (NL)
Synapse (Netherlands) (NL)

Publication Details

Journal: Clinical Neurophysiology
Published: 2025-07-24
DOI: https://doi.org/10.1016/j.clinph.2025.2110937
Citations: 2
Primary Topic: EEG and Brain-Computer Interfaces
Type: article
Field-Weighted Citation Impact: 2.86

Funders

National Health and Medical Research Council

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article

Prediction of response to transcranial magnetic stimulation treatment for depression using EEG and machine learning

Neil W. Bailey, Hanneke van Dijk, Bernadette M. Fitzgibbon, Martijn Arns et al.

Jul 24, 2025

2 citations

Clinical Neurophysiology

EEG and Brain-Computer Interfaces

2.86

article

Prediction of response to transcranial magnetic stimulation treatment for depression using EEG and machine learning

Neil W. Bailey, Hanneke van Dijk, Bernadette M. Fitzgibbon, Martijn Arns, Paul B. Fitzgerald, Ben Fulcher

July 24, 2025article en

2 citations

Abstract

No abstract available for this paper.

Clinical NeurophysiologyVol. 178

Australian National University (AU), The University of Sydney (AU), Maastricht University (NL), Brainclinics (NL), Synapse (Netherlands) (NL)

National Health and Medical Research Council

Good health and well-being

Openalex Percentile: Top 9%

EEG and Brain-Computer Interfaces

2.86

DOI Open Access PDF OpenAlex

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