Decoding of speech information using EEG in children with dyslexia: Less accurate low-frequency representations of speech, not "Noisy" representations

Individual differences in the consistency of neural and behavioural responses to speech sounds

There are documented individual differences among adults in the consistency of speech sound processing, both at neural and behavioural levels. Some adults show more consistent neural responses to speech sounds than others, as measured by an event-related potential called the frequency-following response (FFR); similarly, some adults show more consistent behavioural responses to native speech sounds than others, as measured by two-alternative forced choice (2AFC) and visual analog scaling (VAS) tasks. Adults also differ in how successfully they can perceive non-native speech sounds. Interestingly, it remains unclear whether these differences are related within individuals. In the current study, native English-speaking adults completed native phonetic perception tasks (2AFC and VAS), a non-native (German) phonetic perception task, and an FFR recording session. From these tasks, we derived measures of the consistency of participants' neural and behavioural responses to native speech as well as their non-native perception ability. We then examined the relationships among individual differences in these measures. Analysis of the behavioural measures revealed that more consistent responses to native sounds predicted more successful perception of unfamiliar German sounds. Analysis of neural and behavioural data did not reveal clear relationships between FFR consistency and our phonetic perception measures. This multimodal work furthers our understanding of individual differences in speech processing among adults, and may eventually lead to individualized approaches for enhancing non-native language acquisition in adulthood.

Atypical low-frequency cortical encoding of speech identifies children with developmental dyslexia

Slow cortical oscillations play a crucial role in processing the speech amplitude envelope, which is perceived atypically by children with developmental dyslexia. Here we use electroencephalography (EEG) recorded during natural speech listening to identify neural processing patterns involving slow oscillations that may characterize children with dyslexia. In a story listening paradigm, we find that atypical power dynamics and phase-amplitude coupling between delta and theta oscillations characterize dyslexic versus other child control groups (typically-developing controls, other language disorder controls). We further isolate EEG common spatial patterns (CSP) during speech listening across delta and theta oscillations that identify dyslexic children. A linear classifier using four delta-band CSP variables predicted dyslexia status (0.77 AUC). Crucially, these spatial patterns also identified children with dyslexia when applied to EEG measured during a rhythmic syllable processing task. This transfer effect (i.e., the ability to use neural features derived from a story listening task as input features to a classifier based on a rhythmic syllable task) is consistent with a core developmental deficit in neural processing of speech rhythm. The findings are suggestive of distinct atypical neurocognitive speech encoding mechanisms underlying dyslexia, which could be targeted by novel interventions.

Atypical beta-band effects in children with dyslexia in response to rhythmic audio-visual speech

OBJECTIVE

Previous studies have reported atypical delta phase in children with dyslexia, and that delta phase modulates the amplitude of the beta-band response via delta-beta phase-amplitude coupling (PAC). Accordingly, the atypical delta-band effects in children with dyslexia may imply related atypical beta-band effects, particularly regarding delta-beta PAC. Our primary objective was to explore beta-band oscillations in children with and without dyslexia, to explore potentially atypical effects in the beta band in dyslexic children.

METHODS

We collected EEG data during a rhythmic speech paradigm from 51 children (21 control; 30 dyslexia). We then assessed beta-band phase entrainment, beta-band angular velocity, beta-band power responses and delta-beta PAC.

RESULTS

We found significant beta-band phase entrainment for control children but not for dyslexic children. Furthermore, children with dyslexia exhibited significantly faster beta-band angular velocity and significantly greater beta-band power. Delta-beta PAC was comparable in both groups.

CONCLUSION

Atypical beta-band effects were observed in children with dyslexia. However, delta-beta PAC was comparable in both dyslexic and control children.

SIGNIFICANCE

These findings offer further insights into the neurophysiological basis of atypical rhythmic speech processing by children with dyslexia, suggesting the involvement of a wide range of frequency bands.

Decoding speech information from EEG data with 4-, 7- and 11-month-old infants: Using convolutional neural network, mutual information-based and backward linear models

BACKGROUND

Computational models that successfully decode neural activity into speech are increasing in the adult literature, with convolutional neural networks (CNNs), backward linear models, and mutual information (MI) models all being applied to neural data in relation to speech input. This is not the case in the infant literature.

NEW METHOD

Three different computational models, two novel for infants, were applied to decode low-frequency speech envelope information. Previously-employed backward linear models were compared to novel CNN and MI-based models. Fifty infants provided EEG recordings when aged 4, 7, and 11 months, while listening passively to natural speech (sung or chanted nursery rhymes) presented by video with a female singer.

RESULTS

Each model computed speech information for these nursery rhymes in two different low-frequency bands, delta and theta, thought to provide different types of linguistic information. All three models demonstrated significant levels of performance for delta-band neural activity from 4 months of age, with two of three models also showing significant performance for theta-band activity. All models also demonstrated higher accuracy for the delta-band neural responses. None of the models showed developmental (age-related) effects.

COMPARISONS WITH EXISTING METHODS

The data demonstrate that the choice of algorithm used to decode speech envelope information from neural activity in the infant brain determines the developmental conclusions that can be drawn.

CONCLUSIONS

The modelling shows that better understanding of the strengths and weaknesses of each modelling approach is fundamental to improving our understanding of how the human brain builds a language system.

Atypical speech production of multisyllabic words and phrases by children with developmental dyslexia

The prevalent "core phonological deficit" model of dyslexia proposes that the reading and spelling difficulties characterizing affected children stem from prior developmental difficulties in processing speech sound structure, for example, perceiving and identifying syllable stress patterns, syllables, rhymes and phonemes. Yet spoken word production appears normal. This suggests an unexpected disconnect between speech input and speech output processes. Here we investigated the output side of this disconnect from a speech rhythm perspective by measuring the speech amplitude envelope (AE) of multisyllabic spoken phrases. The speech AE contains crucial information regarding stress patterns, speech rate, tonal contrasts and intonational information. We created a novel computerized speech copying task in which participants copied aloud familiar spoken targets like "Aladdin." Seventy-five children with and without dyslexia were tested, some of whom were also receiving an oral intervention designed to enhance multi-syllabic processing. Similarity of the child's productions to the target AE was computed using correlation and mutual information metrics. Similarity of pitch contour, another acoustic cue to speech rhythm, was used for control analyses. Children with dyslexia were significantly worse at producing the multi-syllabic targets as indexed by both similarity metrics for computing the AE. However, children with dyslexia were not different from control children in producing pitch contours. Accordingly, the spoken production of multisyllabic phrases by children with dyslexia is atypical regarding the AE. Children with dyslexia may not appear to listeners to exhibit speech production difficulties because their pitch contours are intact. RESEARCH HIGHLIGHTS: Speech production of syllable stress patterns is atypical in children with dyslexia. Children with dyslexia are significantly worse at producing the amplitude envelope of multi-syllabic targets compared to both age-matched and reading-level-matched control children. No group differences were found for pitch contour production between children with dyslexia and age-matched control children. It may be difficult to detect speech output problems in dyslexia as pitch contours are relatively accurate.

Neural phase angle from two months when tracking speech and non-speech rhythm linked to language performance from 12 to 24 months

Atypical phase alignment of low-frequency neural oscillations to speech rhythm has been implicated in phonological deficits in developmental dyslexia. Atypical phase alignment to rhythm could thus also characterize infants at risk for later language difficulties. Here, we investigate phase-language mechanisms in a neurotypical infant sample. 122 two-, six- and nine-month-old infants were played speech and non-speech rhythms while EEG was recorded in a longitudinal design. The phase of infants' neural oscillations aligned consistently to the stimuli, with group-level convergence towards a common phase. Individual low-frequency phase alignment related to subsequent measures of language acquisition up to 24 months of age. Accordingly, individual differences in language acquisition are related to the phase alignment of cortical tracking of auditory and audiovisual rhythms in infancy, an automatic neural mechanism. Automatic rhythmic phase-language mechanisms could eventually serve as biomarkers, identifying at-risk infants and enabling intervention at the earliest stages of development.