Related Experiment Video
Updated: Oct 10, 2026

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
Published on: September 8, 2023
Towards a Brain-Computer Interface (BCI) for Improving Phonological Processing in Developmental Dyslexia: An
Xuanci Zheng1, João Araújo1, Quentin Busson1
1Centre for Neuroscience in Education, University of Cambridge, Cambridge, Cambridgeshire, United Kingdom of Great Britain and Northern Ireland.
Abstract:
Brain-computer interfaces (BCIs) have immense potential regarding the provision of therapies for disorders of development, but to date have typically been created for non-linguistic disorders such as ADHD (attention deficit hyperactivity disorder). Here, we present a BCI designed to improve linguistic phonological processing during natural speech listening, aimed at participants with developmental dyslexia. Phonological 'deficits' are considered a core feature of dyslexia across languages and are present from infancy in those later diagnosed with dyslexia. Here, a non-invasive EEG-BCI 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- and post-testing regarding phonological awareness and single word and non-word 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-word 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.

