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Classifying coherent versus nonsense speech perception from EEG using linguistic speech features.
Puffay, Corentin; Vanthornhout, Jonas; Gillis, Marlies; Clercq, Pieter De; Accou, Bernd; Hamme, Hugo Van; Francart, Tom.
Affiliation
  • Puffay C; Department Neurosciences, KU Leuven, ExpORL, Leuven, Belgium. corentin.puffay@kuleuven.be.
  • Vanthornhout J; Department of Electrical engineering (ESAT), KU Leuven, PSI, Leuven, Belgium. corentin.puffay@kuleuven.be.
  • Gillis M; Department Neurosciences, KU Leuven, ExpORL, Leuven, Belgium.
  • Clercq P; Department Neurosciences, KU Leuven, ExpORL, Leuven, Belgium.
  • Accou B; Department Neurosciences, KU Leuven, ExpORL, Leuven, Belgium.
  • Hamme HV; Department Neurosciences, KU Leuven, ExpORL, Leuven, Belgium.
  • Francart T; Department of Electrical engineering (ESAT), KU Leuven, PSI, Leuven, Belgium.
Sci Rep ; 14(1): 18922, 2024 08 14.
Article in En | MEDLINE | ID: mdl-39143297
ABSTRACT
When a person listens to natural speech, the relation between features of the speech signal and the corresponding evoked electroencephalogram (EEG) is indicative of neural processing of the speech signal. Using linguistic representations of speech, we investigate the differences in neural processing between speech in a native and foreign language that is not understood. We conducted experiments using three stimuli a comprehensible language, an incomprehensible language, and randomly shuffled words from a comprehensible language, while recording the EEG signal of native Dutch-speaking participants. We modeled the neural tracking of linguistic features of the speech signals using a deep-learning model in a match-mismatch task that relates EEG signals to speech, while accounting for lexical segmentation features reflecting acoustic processing. The deep learning model effectively classifies coherent versus nonsense languages. We also observed significant differences in tracking patterns between comprehensible and incomprehensible speech stimuli within the same language. It demonstrates the potential of deep learning frameworks in measuring speech understanding objectively.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Speech Perception / Electroencephalography / Language Limits: Adult / Female / Humans / Male Language: En Journal: Sci Rep Year: 2024 Document type: Article Affiliation country: Country of publication:

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Speech Perception / Electroencephalography / Language Limits: Adult / Female / Humans / Male Language: En Journal: Sci Rep Year: 2024 Document type: Article Affiliation country: Country of publication: