{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T05:18:31Z","timestamp":1787980711640,"version":"build-2784847793"},"reference-count":68,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100006376","name":"Georgia Southern University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100006376","id-type":"DOI","asserted-by":"crossref"}]},{"id":[{"id":"https:\/\/ror.org\/04agmb972","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Brain Sci.","Brain Sciences"],"abstract":"<jats:p>Background\/Objectives: Autism spectrum disorder (ASD) and attention-deficit\/hyperactivity disorder (ADHD) are frequently co-occurring neurodevelopmental conditions with partially overlapping neurophysiological profiles. Electroencephalography (EEG) provides non-invasive access to candidate biomarkers, yet the literature remains largely organized around single-diagnosis frameworks, limiting comparison across conditions and constraining translation into intervention selection. This review compares EEG signatures across ASD and ADHD from a transdiagnostic perspective and examines how such signatures might inform the selection of non-pharmacological interventions. Methods: A structured search of PubMed, Scopus, IEEE Xplore and Web of Science identified peer-reviewed studies published between 2010 and 2026 reporting EEG findings in ASD and\/or ADHD, spanning resting-state, task-based, connectivity, event-related potential, machine learning and intervention studies. Sixty-eight sources were synthesized thematically. Given substantial heterogeneity in acquisition parameters and analytic pipelines, evidence was integrated interpretively rather than pooled quantitatively, and no formal risk-of-bias assessment was undertaken. Results: Shared features across both conditions frequently included low-frequency theta excess, reduced alpha modulation under cognitive load, and flattened aperiodic (1\/f) slopes\u2014a pattern compatible with, though not a direct measurement of, altered excitation\/inhibition balance. While substantial heterogeneity exists, disorder-specific signatures often comprised the ASD \u201cU-shaped\u201d spectral profile alongside elevated epileptiform activity, and frontally pronounced theta\/beta ratio elevation in subsets of individuals with ADHD. Machine-learning studies increasingly emphasize interpretable, multidomain feature sets over binary classification. Mindfulness-based and neurofeedback interventions converge on theta reduction and alpha enhancement, although reported effects are frequently conditional on responder status, task context, or outcome-rater blinding. Conclusions: Convergent EEG features support a transdiagnostic account of neurodevelopmental dysregulation. A biomarker-informed framework for intervention selection is proposed, which requires prospective validation before clinical application.<\/jats:p>","DOI":"10.3390\/brainsci16090912","type":"journal-article","created":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T09:00:35Z","timestamp":1787821235000},"page":"912","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Transdiagnostic EEG Signatures in ASD and ADHD: A Comparative Review of Computational Biomarkers and Neuromodulatory Interventions"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-1578-0984","authenticated-orcid":false,"given":"Akshay","family":"Bhuvaneswari Ramakrishnan","sequence":"first","affiliation":[{"name":"School of Computing, Allen E. Paulson College of Engineering and Computing, Georgia Southern University, Statesboro, GA 30460, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nithish Kumar","family":"NavaneethaKrishnan","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Allen E. Paulson College of Engineering and Computing, Georgia Southern University, Statesboro, GA 30460, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"William","family":"Mahler","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Allen E. Paulson College of Engineering and Computing, Georgia Southern University, Statesboro, GA 30460, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adrian","family":"Schoech","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Allen E. Paulson College of Engineering and Computing, Georgia Southern University, Statesboro, GA 30460, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meenalosini","family":"Vimal Cruz","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Allen E. Paulson College of Engineering and Computing, Georgia Southern University, Statesboro, GA 30460, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"De Giacomo, A., Palmieri, R., Russo, E.F., Pizzolorusso, I., Brandi, R., Magistro, F., Di Cesare, M.G., Quattrocelli, S., Cardone, D., and Perpetuini, D. (2026). Machine learning and deep learning applied to EEG and fNIRS for early autism spectrum disorder diagnosis: A systematic review. Front. 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