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Here, we present a BCI designed to improve linguistic phonological processing during natural speech listening, aimed at participants with developmental dyslexia. Phonological \u2018deficits\u2019 are considered a core feature of dyslexia across languages and are present from infancy in those later diagnosed with dyslexia. Here, a non\u2010invasive EEG\u2013BCI relying on auditory inputs and visual feedback was developed to optimise brain patterns related to phonological development in infants and children. The BCI focused on neural patterns identified using Temporal Sampling theory, which has found the theta\/delta ratio during natural speech listening to be a key neural associate of impaired phonology. As a first step, the BCI was tested with adults. Adults with and without dyslexia played the BCI for 16 sessions and received pre\u2010 and post\u2010testing regarding phonological awareness and single word and non\u2010word reading skills. Learning was assessed both online, as in most BCI studies, and offline, enabling removal of a greater number of potential EEG artefacts. Significant associations between offline BCI scores (a measure of BCI learning) and improvements in non\u2010word reading were found for all participants who learned the BCI, both dyslexic and control. BCI learning also showed significant associations with improvements in real word reading and amplitude rise time discrimination. The data are interpreted with respect to Temporal Sampling theory.<\/jats:p>","DOI":"10.1111\/ejn.70709","type":"journal-article","created":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T04:48:44Z","timestamp":1791521324000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Towards a Brain\u2013Computer Interface (BCI) for Improving Phonological Processing in Developmental Dyslexia: An Exploratory Study"],"prefix":"10.1111","volume":"64","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7641-9789","authenticated-orcid":false,"given":"Xuanci","family":"Zheng","sequence":"first","affiliation":[{"name":"Centre for Neuroscience in Education University of Cambridge  Cambridge Cambridgeshire United Kingdom of Great Britain and Northern Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jo\u00e3o","family":"Ara\u00fajo","sequence":"additional","affiliation":[{"name":"Centre for Neuroscience in Education University of Cambridge  Cambridge Cambridgeshire United Kingdom of Great Britain and Northern Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-3438-4673","authenticated-orcid":false,"given":"Quentin","family":"Busson","sequence":"additional","affiliation":[{"name":"Centre for Neuroscience in Education University of Cambridge  Cambridge Cambridgeshire United Kingdom of Great Britain and Northern Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7858-2336","authenticated-orcid":false,"given":"Usha","family":"Goswami","sequence":"additional","affiliation":[{"name":"Centre for Neuroscience in Education University of Cambridge  Cambridge Cambridgeshire United Kingdom of Great Britain and Northern Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,8]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.3389\/feduc.2022.882164"},{"key":"e_1_2_11_3_1","volume-title":"Computational Framework Enabling an EEG\u2010Based BCI for Neurofeedback in Language Disorders: The Case of Dyslexia. Ph.D. Dissertation","author":"Ara\u00fajo J.","year":"2023"},{"key":"e_1_2_11_4_1","volume-title":"Brain\u2010Computer Interface Research, A State of the Art Summary 12","author":"Ara\u00fajo J.","year":"2023"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnhum.2024.1403677"},{"key":"e_1_2_11_6_1","doi-asserted-by":"crossref","unstructured":"Arias F. J. C. 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