Detecting post-stroke aphasia using EEG-based neural envelope tracking of natural speech
[Objective]. After a stroke, one-third of patients suffer from aphasia, a language disorder that impairs communication ability. The standard behavioral tests used to diagnose aphasia are time-consuming and have low ecological validity. Neural tracking of the speech envelope is a promising tool for investigating brain responses to natural speech. The speech envelope is crucial for speech understanding, encompassing cues for processing linguistic units. In this study, we aimed to test the potential of the neural envelope tracking technique for detecting language impairments in individuals with aphasia (IWA). [Approach]. We recorded EEG from 27 IWA in the chronic phase after stroke and 22 controls while they listened to a story. We quantified neural envelope tracking in a broadband frequency range as well as in the delta, theta, alpha, beta, and gamma frequency bands using mutual information analysis. Besides group differences in neural tracking measures, we also tested its suitability for detecting aphasia using a Support Vector Machine (SVM) classifier. We further investigated the required recording length for the SVM to detect aphasia and to obtain reliable outcomes. [Results]. IWA displayed decreased neural envelope tracking compared to controls in the broad, delta, theta, and gamma band. Neural tracking in these frequency bands effectively captured aphasia at the individual level (SVM accuracy 84%, AUC 88%). High-accuracy and reliable detection could be obtained with 5-7 minutes of recording time. [Significance]. Our study shows that neural tracking of speech is an effective biomarker for aphasia. We demonstrated its potential as a diagnostic tool with high reliability, individual-level detection of aphasia, and time-efficient assessment. This work represents a significant step towards more automatic, objective, and ecologically valid assessments of language impairments in aphasia.
Code (0)
등록된 구현이 없습니다.
Tasks
DiagnosticEEGElectroencephalogram (EEG)Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Detecting Post-Stroke Aphasia Via Brain Responses to Speech in a Deep Learning Framework
Aphasia, a language disorder primarily caused by a stroke, is traditionally diagnosed using behavioral language tests. However, these tests are time-consuming, require manual interpretation by trained clinicians, suffer …
EEGA Lesion-aware Edge-based Graph Neural Network for Predicting Language Ability in Patients with Post-stroke Aphasia
We propose a lesion-aware graph neural network (LEGNet) to predict language ability from resting-state fMRI (rs-fMRI) connectivity in patients with post-stroke aphasia. Our model integrates three components: an edge-base…
Functional ConnectivityGraph Neural NetworkProsody of speech production in latent post-stroke aphasia
This study explores prosodic production in latent aphasia, a mild form of aphasia associated with left-hemisphere brain damage (e.g. stroke). Unlike prior research on moderate to severe aphasia, we investigated latent ap…
DiagnosticZero-Shot Cross-lingual Aphasia Detection using Automatic Speech Recognition
Aphasia is a common speech and language disorder, typically caused by a brain injury or a stroke, that affects millions of people worldwide. Detecting and assessing Aphasia in patients is a difficult, time-consuming proc…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Language ModelingLanguage Modelling+2Language rehabilitation of people with BROCA aphasia using deep neural machine translation
More than 13 million people suffer a stroke each year. Aphasia is known as a language disorder usually caused by a stroke that damages a specific area of the brain that controls the expression and understanding of langua…
Machine TranslationTranslation