Diagnosing aphasia by EEG-based neural tracking of natural speech

More than 1 million Europeans suffer a stroke each year, and one third of them is faced with aphasia, i.e. a language disorder with heterogeneous language profiles, varying in the degree to which each component of the language processing chain (i.e. auditory, phonological, semantic and syntactic processing) is affected. Although a reliable detection and in-depth characterization of the affected component is vital for targeted intervention, behavioral language testing is often difficult due to co-morbid cognitive problems in persons with aphasia. Recently, we developed a EEG analysis technique that can capture, at least in neurological healthy persons, how natural speech is processed at each of the different linguistic components, resulting in a subject-specific language profile. With our project, we aim to apply this technique in aphasia. This will allow to (1) identify language problems in aphasia and distinguish it from cognitive problems, (2) determine individual language profiles for persons with aphasia, and (3) provide information on whether neural restoration or compensation mechanisms are used. In contrast to previous EEG studies on aphasia, our method  uses ecologically valid stimuli (natural speech) and requires only one paradigm to asses each component of the language processing chain. Hence, there is a high clinical application potential.