The TDBRAIN (Neurophysiology Knowledge Research Archive) database of two decades of brainclinics

  • Berger, H. Über das Elektrenkephalogramm des Menschen. Archiv Für Psychiatrie Und Nervenkrankheiten 87527–570 (1929).

    Google Scholar article

  • Wige, AS et al. Electroencephalographic biomarkers for predicting treatment response in major depressive illness: a meta-analysis. Am J Psychiatry 17644–56 (2019).

    Google Scholar article

  • Ioannidis, JPA Why most published research results are wrong. Plos-Med 2e124 (2005).

    Google Scholar article

  • van der Vinne, N., Vollebregt, MA, van Putten, MJAM, and Arns, M. Frontal alpha asymmetry as a diagnostic marker for depression: fact or fiction? A meta-analysis. Clin Neuroimage 1679–87 (2017).

    Google Scholar article

  • Arns, M., Conners, CK & Kraemer, HC A decade of research on the EEG theta/beta ratio in ADHD. J Wait Mess 17374–383 (2013).

    Google Scholar article

  • Bailey, N.W. 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 Neurophysiolhttps://doi.org/10.1016/j.clinph.2020.10.018 (2020).

  • Roeloffs, C. et al. Individual alpha frequency proximity associated with outcome of repetitive transcranial magnetic stimulation: an independent replication study from the ICON-DB consortium. Clin Neurophysiol S1388-245730532–0 (2020).

    Google Scholar

  • Putten, MJAM, van, Olbrich, S. & Arns, M. Predicting sex from brain rhythms with deep learning. Sci Rep-UK 83069 (2018).

    Article on Google Scholar Ads

  • Gemein, LAW et al. Machine learning based diagnosis of EEG pathology. (2020).

  • Tjepkema-Cloostermans, MC, Carvalho, RCVD & Putten, MJAMV Deep learning for the detection of focal epileptiform discharges from scalp EEG recordings. Clin Neurophysiol https://doi.org/10.1016/j.clinph.2018.06.024 (2018).

    PubMed Google Scholar article

  • Khodayari-Rostamabad, A., Reilly, JP, Hasey, GM, de Bruin, H. & MacCrimmon, DJ A machine learning approach using EEG data to predict response to SSRI treatment for major depressive disorder. Clin Neurophysiol 1241975-1985 (2013).

    Google Scholar article

  • Roy, Y. et al. Deep learning-based electroencephalographic analysis: a systematic review. Neural Engineering Journal 16051001 (2019).

    Article on Google Scholar Ads

  • Hosseini, M.-P., Hosseini, A. & Ahi, K. A review of machine learning for EEG signal processing in bioengineering. Ieee Rev Biomed Eng PP, 1–1 (2020).

  • Adrian, ED & Matthews, BHC The shepherd rhythm: potential changes in the occipital lobes in man. Brain 57355–385 (1934).

    Google Scholar article

  • Katada, A., Ozaki, H., Suzuki, H., and Suhara, K. Developmental EEG characteristics of normal and mentally retarded children. Electroen Clin Neuro 52192–201 (1981).

    CAS Google Scholar Article

  • Smith, JR The electroencephalogram during normal infancy and childhood: I. Rhythmic activities present in the newborn and their later development. Pedagogical seminar J Genetic Psychology 53431–453 (1938).

    Google Scholar article

  • Smith, JR The electroencephalogram in normal infancy and childhood: II. The nature of Alpha wave growth. Pedagogical seminar J Genetic Psychology 53455–469 (1938).

    Google Scholar article

  • Arns, M. et al. EEG alpha asymmetry as a gender-specific predictor of outcome of acute treatment with different antidepressants in the randomized iSPOT-D study. Clin Neurophysiol 127509–19 (2016).

    Google Scholar article

  • Williams, LM et al. The test-retest reliability of a battery of standardized neurocognitive and neurophysiological tests: “neuromarker”. Int J Neurosci 1151605–1630 (2005).

    CAS Google Scholar Article

  • Paul, HR et al. Cross-cultural assessment of neuropsychological performance and measures of electrical brain function: further validation of an international brain database. Int J Neurosci 117549-568 (2009).

    Google Scholar article

  • Clark, C. et al. Standardized assessment of cognitive functioning during development and aging using an automated touchscreen battery. Arch Clin Neuropsych 21449–467 (2006).

    Google Scholar article

  • van Dijk, H. et al. Two Decades – Brainclinics Research Archive for Insights in Neuroscience (TD-BRAIN), Synapsehttps://doi.org/10.7303/syn25671079 (2021).

  • Donse, L., Padberg, F., Sack, AT, Rush, AJ & Arns, M. Simultaneous rTMS and psychotherapy in major depressive disorder: clinical outcomes and predictors from a large naturalistic study. brain stimulation 11337–345 (2017).

    Google Scholar article

  • Dozois, DJA, Dobson, KS & Ahnberg, JL A psychometric evaluation of the Beck Depression Inventory-II. Psychological assessment ten83–89 (1998).

    Google Scholar article

  • Krepel, N. et al. A multicenter efficacy trial of QEEG-informed neurofeedback in ADHD: replication and treatment prediction. Clin Neuroimage 102399, https://doi.org/10.1016/j.nicl.2020.102399 (2020).

  • Sandra Kooij, JJ et al. Reliability, validity, and usefulness of self-report and informant reporting instruments regarding ADHD symptoms in adult patients. J Wait Mess 11445–458 (2008).

    CAS Google Scholar Article

  • Donse, L., Sack, AT, Fitzgerald, PB & Arns, M. Sleep disturbances in obsessive-compulsive disorder: association with nonresponse to repetitive transcranial magnetic stimulation (rTMS). J Anxiety disorder 4931–39 (2017).

    Google Scholar article

  • Sheehan, DV et al. The International Neuropsychiatric Mini-Interview (MINI): The Development and Validation of a Structured Psychiatric Diagnostic Interview for DSM-IV and ICD-10. J Clin Psychiatry 59 Suppl 20, 22–33, quiz 34-57 (1998).

  • Pernet, CR et al. EEG-BIDS, an extension of brain imaging data structure for electroencephalography. Scientific data 6103 (2019).

    Google Scholar article

  • Harris, CR et al. Array programming with NumPy. Nature 585357–362 (2020).

    ADS CAS Article Google Scholar

  • Virtanen, P. et al. SciPy 1.0: Fundamental algorithms for scientific computing in Python. Natural methods 17261-272 (2020).

    CAS Google Scholar Article

  • Gratton, G., Coles, MGH, and Doncin, E. A new method for offline removal of ocular artifacts. Electroencephalogram Clin Neurophysiol 55468–484 (1983).

    CAS Google Scholar Article

  • Alschuler, DM, Tenke, CE, Bruder, GE, and Kayser, J. Identification of electrode bridging from electrical distance distributions: a survey of publicly available EEG data using a new method. Clin Neurophysiol 125484–90 (2014).

    Google Scholar article

  • Oostenveld, R., Fries, P., Maris, E. & Schoffelen, J.-M. FieldTrip: open source software for advanced analysis of MEG, EEG and invasive electrophysiology. Data. Intel Neurosc computing 2011156869 (2011).

    Google Scholar