Unravelling speech processing mechanisms in aphasia via EEG-based neural tracking and behavioral measures

After a stroke, about one third of the patients is faced with aphasia, a language disorder affecting speech comprehension and production. Persons with aphasia show very heterogeneous symptoms and vary in the degree to which each level of the speech processing chain, spectrotemporal, phonological or semantic processing, is affected. Although a precise diagnosis is vital for targeted intervention, behavioral testing can be very challenging due to communication problems as well as potential co-morbid cognitive problems. The aim of this project is to decode brain signal from EEG measures while patients listen to natural speech signals, that is a 30-minute-long story. We suggest to (1) detect aphasia with EEG, (2) determine in which processing stages resides the locus of the language disorder and (3) cluster patients with similar profiles and link them to behavioural results. Neural coding of the brain signals might improve the characterization of the language problems in stroke patients with aphasia, above and beyond behavioral measures.