2024
DOI: 10.1016/j.jneumeth.2023.110036
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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

Mahmoud Keshavarzi,
Áine Ní Choisdealbha,
Adam Attaheri
et al.
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Cited by 4 publications
(2 citation statements)
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“…Grammar measures were included in the UK BabyRhythm study, but did not show sufficient individual differences for neural prediction analyses (Rocha et al, 2024). Nevertheless, these prior TSF-driven infant data suggest that both delta-band phase angle and theta-gamma PAC in the current study may be associated with DLD status.…”
Section: Introductionmentioning
confidence: 79%
See 1 more Smart Citation
“…Grammar measures were included in the UK BabyRhythm study, but did not show sufficient individual differences for neural prediction analyses (Rocha et al, 2024). Nevertheless, these prior TSF-driven infant data suggest that both delta-band phase angle and theta-gamma PAC in the current study may be associated with DLD status.…”
Section: Introductionmentioning
confidence: 79%
“…If neural temporal prediction of stressed syllable placement is important developmentally for language acquisition, then it might also be expected a priori that dynamic relations (cross-frequency coupling) between the electrophysiological delta and theta bands may be atypical in children with DLD. In the infant speech rhythm studies (Ni Choisdealbha, 2022, nursery rhymes were also sung to the participating infants while EEG was recorded (Attaheri et al, 2022;Keshavarzi et al, 2024b). While the accuracy of delta band cortical tracking predicted better language outcomes, greater theta power and a higher excitation/inhibition ratio between theta and delta predicted worse language outcomes (Attaheri et al, preprint).…”
Section: Introductionmentioning
confidence: 99%