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Updated: Mar 17, 2026
Infant Auditory Processing and Event-related Brain Oscillations
Published on: July 1, 2015
Speech Auditory Brainstem Response to Predict Language Delay
Patrick C M Wong1,2, Shaoqi Pan1,3, Ching Man Lai1
1Brain and Mind Institute, The Chinese University of Hong Kong, Hong Kong SAR, China.
Insights
Neural speech encoding using electroencephalography (EEG) in infants can predict language delay. This approach offers a novel screening tool for early intervention, improving language development outcomes.
Area of Science:
- Neuroscience
- Developmental Psychology
- Speech-Language Pathology
Background:
- Preterm birth is a known predictor of language delay but lacks precision for individual child assessment.
- Early intervention (EI) is highly effective for language delay but requires accurate, child-level prediction.
- Current predictive methods are insufficient for timely EI prescription before preschool.
Purpose of the Study:
- To develop and validate predictive models for language delay using infant neural data.
- To forecast language delay to enable preemptive early intervention.
- To utilize electroencephalography (EEG) neural speech encoding for early identification of at-risk children.
Main Methods:
- Recorded EEG neural speech encoding (speech auditory brainstem response [ABR]) from 423 infants (1-24 months).
- Collected language outcomes using the Bayley Scales of Infant and Toddler Development (7-32 months).
- Employed random forest models to compare predictive accuracy with and without EEG measures.
Main Results:
- Models incorporating EEG measures significantly outperformed non-neural clinical predictors.
- EEG-only models achieved >90% sensitivity and AUC, with sustained >80% sensitivity and >90% AUC upon external validation.
- Non-neural models (gestational age, birth weight) predicted outcomes above chance but were less accurate than EEG models.
Conclusions:
- Speech ABR shows promise as a novel screening tool for identifying infants at risk of language delay.
- Early identification via speech ABR enables timely EI, potentially enhancing language development.
- This neural-based approach offers a more precise method for child-level prediction compared to existing methods.
Objective:
Although preterm birth is among the best predictors of language delay, it is not precise enough to make child-level prediction that will enable the prescription of the highly effective early intervention (EI). This study aims to develop and validate predictive models of language delay using neural data collected from as early as infancy to forecast language delay as to indicate EI before preschool years.
Method:
Electroencephalography (EEG) neural speech encoding (ie, "speech auditory brainstem response [ABR]") was recorded from 423 Chinese-learning infants between 1 and 24 months, and language outcomes were collected from the same children between 7 and 32 months in this cohort study. Data were collected from 2016 to 2024, with an analysis cutoff on October 3, 2024. Early-latency and long-latency EEG responses to 3 speech stimuli (2 native, 1 non-native) were collected. Model outcome was the language subscale of the Bayley Scales of Infant and Toddler Development, third edition.
Results:
Random forest was used to classify children into binary groups based on outcome measure: below/at the 16th percentile vs above. Different predictive models were constructed and compared, including those with and without EEG and clinical measures. Models with non-neural measures (eg, gestational age and birth weight) predicted language outcomes above chance. Models with EEG measures alone outperformed any non-neural models, achieving sensitivity and area under the receiver operating characteristic curve (AUC) well above 90% for the best models. When EEG models were externally validated, sensitivity and AUC remained above 80% and 90%, respectively.
Conclusion:
Speech ABR can be a novel screening tool for language delay, allowing families of screened children to adopt EI preemptively for enhanced language development.
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