{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T22:01:24Z","timestamp":1789164084401,"version":"build-2803163510"},"reference-count":320,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T00:00:00Z","timestamp":1785715200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurosci."],"abstract":"<jats:sec>\n                    <jats:title>Introduction and objective<\/jats:title>\n                    <jats:p>This article provides a comprehensive review of the methodology of Quantitative Electroencephalography (QEEG) as an advanced tool in modern neurodiagnostics. The objective of this study is to systematize knowledge regarding the technical aspects of signal acquisition, its specific clinical applications, and the interpretative frameworks that determine the efficacy of personalized therapeutic interventions in psychiatry and neurology.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>This paper constitutes a critical review of the relevant literature. The study analyzed a total of 321 bibliographic sources, peer-reviewed empirical studies, systematic reviews, and supplementary scientific book chapters, published between 1932 and the first half of 2026.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The analysis of the gathered evidence demonstrates that QEEG has the potential to objectify the neurophysiological phenomena underlying clinical presentations. In neurodevelopmental disorders such as ADHD and ASD, this method may support the identification of electrophysiological subtypes and connectivity dysfunctions (e.g., the coexistence of hypo- and hypercoherence in autism). The technique has shown potential utility in differential diagnosis and the decomposition of overlapping symptoms. This includes unmasking hidden compensatory mechanisms in high-functioning patients with ADHD, which often manifest as hyperactivity in the Beta band. QEEG provides promising adjunctive biomarkers in affective disorders (such as Frontal Alpha Asymmetry - FAA) and anxiety disorders (characterized by an excess of fast waves). It is also being investigated as a potential adjunctive tool in detecting the early stages of neurodegeneration through a decrease in peak Alpha frequency and in exploring post-COVID syndromes. Furthermore, identifying individualized network profiles allows for the objective personalization of neuromodulatory therapies including Neurofeedback, rTMS, and tDCS. It also facilitates predicting and monitoring responses to pharmacotherapy, such as the innovative treatment of epilepsy with cannabidiol (CBD).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>QEEG is a promising translational tool that may help elevate neurodiagnostics from the level of subjective behavioral assessment to objective and measurable neurobiological indicators. Implementing this method in clinical practice serves as a valuable adjunctive tool to support diagnostic sensitivity and the development of personalized treatment strategies. It is hypothesized that this approach may potentially shorten the time required to achieve remission and minimize the risk of polypharmacy, though these clinical benefits require further validation through prospective controlled studies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fnins.2026.1834391","type":"journal-article","created":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T05:31:20Z","timestamp":1785735080000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Quantitative electroencephalography as a next-generation tool in neurodiagnostics: significance, clinical applications, and practical interpretative frameworks"],"prefix":"10.3389","volume":"20","author":[{"given":"Marta","family":"Kopa\u0144ska","sequence":"first","affiliation":[{"name":"Department of Medical Psychology, Faculty of Medicine, University of Rzeszow","place":["Rzesz\u00f3w, Poland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julia","family":"Trojniak","sequence":"additional","affiliation":[{"name":"Student Research Club \u201cReh-Tech\u201d, Faculty of Medicine, University of Rzesz\u00f3w","place":["Rzesz\u00f3w, Poland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria","family":"Pachalska","sequence":"additional","affiliation":[{"name":"Department of Neuropsychology and Neurorehabilitation, Andrzej Frycz Modrzewski Krakow University","place":["Cracow, Poland"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,3]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"123991","DOI":"10.1016\/j.eswa.2024.123991","article-title":"EEG based automated detection of seizure using machine learning approach and traditional features","volume":"251","author":"Abhishek","year":"2024","journal-title":"Expert Syst. 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