Towards Robust,Reproducible,and Clinically Actionable EEG Biomarkers: Large Open Access EEG Database for Discovery and Out-of-sample Validation

Piller C . Blots on a field? Science Features. 2022;377:358–363.

Ioannidis JPA . Why most published research findings are false. PLoS Med. 2005;2(8):e124. DOI: 10.1371/journal.pmed.0020124.

Widge AS Bilge MT Montana R , 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.

Arns M Conners CK Kraemer HC . 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.

Vinne Nvd Vollebregt MA Putten Mv , 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.

Arns M Drinkenburg WH Fitzgerald PB , 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.

Krepel N Sack AT Kenemans JL , 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.

Voetterl H Wingen Gv Michelini G , 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.

Bailey NW Krepel N Dijk Hv , 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.

Roelofs C Krepel N Corlier J , 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.

Putten Mv Olbrich S Arns M . Predicting sex from brain rhythms with deep learning. Sci Rep. 2018;8(1):3069. DOI: 10.1038/s41598-018-21495-7.

Gemein LAW Schirrmeister RT Chrabąszcz P , et al. 2020. Machine-Learning-Based Diagnostics of EEG Pathology.

Tjepkema-Cloostermans MC Carvalho RCVD Putten MJAMV . Deep learning for detection of focal epileptiform discharges from scalp EEG recordings. Clin Neurophysiol. 2018;129:2191–2196.

Khodayari-Rostamabad A Reilly JP Hasey GM , 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.

Hosseini M-P Hosseini A Ahi K . A review on machine learning for EEG signal processing in bioengineering. IEEE Rev Biomed Eng, 2019;14:204–218.

Roy Y Banville H Albuquerque I , et al. Deep learning-based electroencephalography analysis: a systematic review. J Neural Eng. 2019;16(5):051001. DOI: 10.1088/1741-2552/ab260c.

Dijk Hv Wingen Gv Denys D , 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.