EEG based depression detection by machine learning: Does inner or overt speech condition provide better biomarkers when using emotion words as experimental cues?
While the generalizability of our results is limited by the small sample size and potentially uncontrolled confounders, depression was associated with sensitive and presumably network-like aspects of these brain areas, potentially implying a higher level of emotion regulation that increases primarily in open communication.
Authors
- Gábor Révy (ORCID: https://orcid.org/0000-0002-4547-3923)
- Gábor Hullám (ORCID: https://orcid.org/0000-0002-4765-2351)
- Mihály Vetró (ORCID: https://orcid.org/0009-0001-3354-435X)
- Máté Kapitány‐Fövény (ORCID: https://orcid.org/0000-0002-0850-8699)
- Dániel Fabó (ORCID: https://orcid.org/0000-0001-5141-5351)
- Danuta Szirmai (ORCID: https://orcid.org/0009-0001-3468-600X)
Institutions
- Semmelweis University (HU)
- Budapest University of Technology and Economics (HU)
- National Institute of Menthal Health, Neurology and Neurosurgery - Nyírő Gyula Hospital (HU)
- HUN-REN Research Centre for Natural Sciences (HU)
Publication Details
- Journal
- Journal of Psychiatric Research
- Published
- 2024-08-05
- DOI
- https://doi.org/10.1016/j.jpsychires.2024.08.002
- Citations
- 8
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 2.74