Should the EEG–Based Theta to Beta Ratio Be Used to Diagnose ADHD?

Abstract

Should the EEG–Based Theta to Beta Ratio Be Used to Diagnose ADHD?Sandra K. Loo and Martijn ArnsSandra K. LooDr. Loo can be contacted by e-mail at [email protected]Search for more papers by this author and Martijn Arns1 Associate Professor-in-Residence in Psychiatry and Biobehavioral Sciences at the Brain Research Institute of the David Geffen School of Medicine, University of California, Los Angeles.2 Research Institute Brainclinics, Nijmegen, Utrecht University, Department of Experimental Psychology, and neuroCare Group, Nijmegen, all in The Netherlands.Search for more papers by this authorPublished Online:December 2015https://doi.org/10.1521/adhd.2015.23.8.8PDFPDF PLUS ShareShare onFacebookTwitterLinkedInRedditEmail ToolsAdd to favoritesDownload CitationsTrack Citations About Previous article Next article FiguresReferencesRelatedDetails Cited byCited by1. Spectral parameterization for studying neurodevelopment: How and whyOnline publication date: Go to citation Crossref Google Scholar2. Psychopathy and Resting State EEG Theta/Beta Oscillations in Adolescent OffendersOnline publication date: 10 January 2022. Go to citation Crossref Google Scholar3. Machine learning models effectively distinguish attention-deficit/hyperactivity disorder using event-related potentialsOnline publication date: 15 February 2022. Go to citation Crossref Google Scholar4. The Role of Quantitative Electroencephalogram in the Diagnosis and Subgrouping of Attention-Deficit/Hyperactivity DisorderOnline publication date: Go to citation Crossref Google Scholar5. Measuring Treatment Response in Pharmacological and Lifestyle Interventions Using Electroencephalography in ADHD: A ReviewOnline publication date: 9 January 2019. Go to citation Crossref Google Scholar6. Classification Accuracy of Neuroimaging Biomarkers in Attention-Deficit/Hyperactivity Disorder: Effects of Sample Size and Circular AnalysisOnline publication date: Go to citation Crossref Google Scholar7. Addiction and Quantitative ElectroencephalographyOnline publication date: Go to citation Crossref Google Scholar8. Classification of ADHD and non-ADHD subjects using a universal background modelOnline publication date: Go to citation Crossref Google Scholar9. Classification of ADHD and Non-ADHD using theta/beta power ratio featuresOnline publication date: Go to citation Crossref Google Scholar10. EEG channel selection for AR model based ADHD classificationOnline publication date: Go to citation Crossref Google Scholar Volume 23Issue 8Dec 2015 Information© 2015 The Guilford PressPDF download