PillerC. Blots on a field?Science Features. 2022;377:358–363.
2.
IoannidisJPA. Why most published research findings are false. PLoS Med.2005;2(8):e124. DOI: 10.1371/journal.pmed.0020124.
3.
WidgeASBilgeMTMontanaR, et al.Electroencephalographic biomarkers for treatment response prediction in major depressive illness: a meta-analysis. Am J Psychiatry.2019;176(1):44‐56. The American journal of psychiatry. DOI: 10.1176/appi.ajp.2018.17121358.
4.
ArnsMConnersCKKraemerHC. A decade of EEG Theta/Beta ratio research in ADHD. J Atten Disord.2013;17(5):374‐383. Journal of Attention Disorders. DOI: 10.1177/1087054712460087.
5.
VinneNvdVollebregtMAPuttenMv, et al.Frontal alpha asymmetry as a diagnostic marker in depression: fact or fiction? A meta-analysis. Neuroimage Clin. 2017;16:79‐87. (Biol. Psychol. 67 1–2 2004). DOI: 10.1016/j.nicl.2017.07.006.
6.
ArnsMDrinkenburgWHFitzgeraldPB, et al.Neurophysiological predictors of non-response to rTMS in depression. Brain Stimul.2012;5(4):569‐576. DOI: 10.1016/j.brs.2011.12.003.
7.
KrepelNSackATKenemansJL, et al.Non-replication of neurophysiological predictors of non-response to rTMS in depression and neurophysiological data-sharing proposal. Brain Stimul.2018;11(3):639‐641. DOI: 10.1016/j.brs.2018.01.032.
8.
VoetterlHWingenGvMicheliniG, et al.Brainmarker-I differentially predicts remission to various attention-deficit/hyperactivity disorder treatments: a blinded discovery, transfer and validation study. Biological Psychiatry: Cognitive Neuroscience Neuroimaging. 2022. DOI: 10.1016/j.bpsc.2022.02.007.
9.
BaileyNWKrepelNDijkHv, et al.Resting EEG theta connectivity and alpha power to predict repetitive transcranial magnetic stimulation response in depression: a non-replication from the ICON-DB consortium. Clin Neurophysiol.2020;132: 650–659. DOI: 10.1016/j.clinph.2020.10.018.
10.
RoelofsCKrepelNCorlierJ, et al.Individual alpha frequency proximity associated with repetitive transcranial magnetic stimulation outcome: an independent replication study from the ICON-DB consortium. Clinical Neurophysiology . 2020;S1388-2457(20):30532‐0. DOI: 10.1016/j.clinph.2020.10.017.
11.
PuttenMvOlbrichSArnsM. Predicting sex from brain rhythms with deep learning. Sci Rep.2018;8(1):3069. DOI: 10.1038/s41598-018-21495-7.
12.
GemeinLAWSchirrmeisterRTChrabąszczP, et al.2020. Machine-Learning-Based Diagnostics of EEG Pathology.
13.
Tjepkema-CloostermansMCCarvalhoRCVDPuttenMJAMV. Deep learning for detection of focal epileptiform discharges from scalp EEG recordings. Clin Neurophysiol. 2018;129:2191–2196.
14.
Khodayari-RostamabadAReillyJPHaseyGM, et al.A machine learning approach using EEG data to predict response to SSRI treatment for major depressive disorder. Clin Neurophysiol.2013;124(10):1975‐1985. DOI: 10.1016/j.clinph.2013.04.010.
15.
HosseiniM-PHosseiniAAhiK. A review on machine learning for EEG signal processing in bioengineering. IEEE Rev Biomed Eng,2019;14:204–218.
16.
RoyYBanvilleHAlbuquerqueI, et al.Deep learning-based electroencephalography analysis: a systematic review. J Neural Eng.2019;16(5):051001. DOI: 10.1088/1741-2552/ab260c.
17.
DijkHvWingenGvDenysD, et al.The two decades brainclinics research archive for insights in neurophysiology (TDBRAIN) database. Sci Data.2022;9(1):333. DOI: 10.1038/s41597-022-01409-z.