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 from low ecological validity, and diagnosis can be biased by comorbid motor and cognitive problems present in aphasia. In this study, we introduce an automated screening tool for speech processing impairments in aphasia that relies on time-locked brain responses to speech, known as neural tracking, within a deep learning framework. We modeled electroencephalography (EEG) responses to acoustic, segmentation, and linguistic speech representations of a story using convolutional neural networks trained on a large sample of healthy participants, serving as a model for intact neural tracking of speech. Subsequently, we evaluated our models on an independent sample comprising 26 individuals with aphasia (IWA) and 22 healthy controls. Our results reveal decreased tracking of all speech representations in IWA. Utilizing a support vector machine classifier with neural tracking measures as input, we demonstrate high accuracy in aphasia detection at the individual level (85.42\%) in a time-efficient manner (requiring 9 minutes of EEG data). Given its high robustness, time efficiency, and generalizability to unseen data, our approach holds significant promise for clinical applications.

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.