A
Adam Attaheri
Researcher at University of Cambridge
Publications - 5
Citations - 119
Adam Attaheri is an academic researcher from University of Cambridge. The author has contributed to research in topics: Electroencephalography & Speech processing. The author has an hindex of 2, co-authored 5 publications receiving 41 citations. Previous affiliations of Adam Attaheri include Newcastle University.
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Delta- and theta-band cortical tracking and phase-amplitude coupling to sung speech by infants
Adam Attaheri,Áine Ní Choisdealbha,Giovanni M. Di Liberto,Sinead Rocha,Perrine Brusini,Natasha Mead,Helen Olawole-Scott,Panagiotis Boutris,Samuel Gibbon,Isabel Williams,Christina Grey,Sheila Flanagan,Usha Goswami +12 more
TL;DR: Cortical speech tracking via delta & theta neural signals (mTRF) is demonstrated and Delta and theta driven phase amplitude coupling (PAC) was found at all ages Gamma frequency amplitudes displayed stronger PAC to low frequency phases than beta.
Journal ArticleDOI
Evolutionary origins of non-adjacent sequence processing in primate brain potentials.
Alice E. Milne,Jutta L. Mueller,Jutta L. Mueller,Claudia Männel,Adam Attaheri,Adam Attaheri,Angela D. Friederici,Christopher I. Petkov +7 more
TL;DR: Monkey ERPs show early pitch and rule deviant mismatch responses that are strikingly similar to those previously reported in human infants, and provide evidence for evolutionarily conserved neurophysiological effects, some of which are remarkably like those seen at an early human developmental stage.
Journal ArticleDOI
Delta- and theta-band cortical tracking and phase-amplitude coupling to sung speech by infants.
Adam Attaheri,Áine Ní Choisdealbha,Giovanni M. Di Liberto,Sinead Rocha,Perrine Brusini,Natasha Mead,Helen Olawole-Scott,Panagiotis Boutris,Samuel Gibbon,Isabel Williams,Christina Grey,Sheila Flanagan,Usha Goswami +12 more
TL;DR: In this paper, the authors examined the presence and maturation of low-frequency (<12 Hz) cortical speech tracking in infants by recording EEG longitudinally from 60 participants when aged 4-, 7- and 11- months as they listened to nursery rhymes.
Journal ArticleDOI
Machine learning accurately classifies neural responses to rhythmic speech vs. non-speech from 8-week-old infant EEG.
Samuel Gibbon,Adam Attaheri,Áine Ní Choisdealbha,Sinead Rocha,Perrine Brusini,Natasha Mead,Panagiotis Boutris,Helen Olawole-Scott,Henna Ahmed,Sheila Flanagan,Kanad Mandke,Mahmoud Keshavarzi,Usha Goswami +12 more
TL;DR: In this paper, the authors investigate whether infant brain responses to rhythmic stimuli can be classified reliably using EEG from 95 eight-week-old infants listening to natural stimuli (repeated syllables or drumbeats).