Detecting Post-Stroke Aphasia Via Brain Responses to Speech in a Deep Learning Framework

TL;DR

This paper presents an automated, EEG-based deep learning method for detecting post-stroke aphasia by analyzing neural responses to speech, offering a quick and accurate clinical screening tool.

Contribution

Introduces a novel deep learning framework utilizing neural tracking of speech in EEG data for automated aphasia detection, outperforming traditional behavioral tests.

Findings

High accuracy (85.42%) in individual aphasia detection

Effective neural tracking of speech representations in aphasic patients

Time-efficient screening requiring only 9 minutes of EEG data

Abstract

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.