Detecting post-stroke aphasia
using EEG-based neural envelope tracking
of natural speech

Pieter De Clercq1, Jill Kries1, Ramtin Mehraram1, Jonas Vanthornhout1, Tom
Francart1 and Maaike Vandermosten1

1: Experimental Oto-Rhino-Laryngology, Department of Neurosciences, Leuven
Brain Institute, KU Leuven, Belgium

Version:
March 2023

Corresponding author:
Pieter De Clercq<EMAIL_ADDRESS>
## Abstract

After a stroke, approximately 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, require subjective
interpretation, and have low ecological validity. As a consequence, comorbid
cognitive problems present in individuals with aphasia (IWA) can bias test
results, generating a discrepancy between test outcomes and everyday-life
language abilities. Neural tracking of the speech envelope is a promising tool
for investigating brain responses to natural speech. The envelope of speech is
crucial for speech understanding, encompassing cues for detecting and
segmenting linguistic units, e.g., phrases, words and phonemes. In this study,
we aimed to test the potential of the neural envelope tracking technique for
detecting language impairments in IWA.

We recorded EEG from 27 IWA in the chronic phase after stroke and 22 healthy
controls while they listened to a 25-minute 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 at the individual level 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.

IWA displayed decreased neural envelope tracking compared to healthy controls
in the broad, delta, theta, and gamma band, which is in line with the assumed
role of these bands in auditory and linguistic processing of speech. Neural
tracking in these frequency bands effectively captured aphasia at the
individual level, with an SVM accuracy of 84% and an area under the curve of
88%. Moreover, we demonstrated that high-accuracy detection of aphasia can be
achieved in a time-efficient (5 minutes) and highly reliable manner (split-
half reliability correlations between R=0.62 and R=0.96 across frequency
bands).

Our study shows that neural envelope tracking of natural speech is an
effective biomarker for language impairments in post-stroke 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.

Keywords: Aphasia, natural speech processing, neural envelope tracking,
diagnostics

##  1 Introduction

Aphasia is an acquired language disorder impairing communication ability and
is principally caused by a stroke in the language-dominant left hemisphere
(Papathanasiou and Coppens,, 2017). The current practice is to diagnose
aphasia by means of behavioral language tests. However, these tests suffer
from influences of co-morbid motor and cognitive problems (Rohde et al.,,
2018) and of a low ecological validity (Devanga et al.,, 2021; Wallace and
Kimelman,, 2013). Novel analysis techniques for EEG-data, i.e., neural
tracking of speech (e.g., see Lalor et al., (2009); Brodbeck et al., (2022);
Crosse et al., (2021)), allow measuring brain responses while participants
listen to natural speech, providing an ecologically valid way to measure
speech processing. In this study, we test the potential of these novel EEG
analyses for detecting language impairments in aphasia with high accuracy and
in a time-efficient way.

The current standard for diagnosing aphasia is based on performance on
behavioral language tests, such as the Western Aphasia Battery (Kertesz,,
1982), the Token test (de Renzi and Ferrai,, 1978) or a picture-naming test
(Van Ewijk et al.,, 2020). Yet, these tests have several disadvantages. First,
behavioral testing is time-consuming, requiring active cooperation of the
patient and scoring by the clinician. Second, behavioral assessment can lead
to inaccurate initial diagnoses due to concomitant motor, memory, attention
and executive impairments (Rohde et al.,, 2018), reportedly affecting over 80%
of individuals with aphasia (IWA) (El Hachioui et al.,, 2014). Finally,
language tests consist of rather artificial tasks in which sounds, phonemes,
words or short sentences are presented in isolation. This contrasts with
natural speech processing where language components interact, and higher-level
context integration takes place (Hamilton and Huth,, 2018; Kandylaki and
Bornkessel-Schlesewsky,, 2019). Consequently, there is a discrepancy between
clinical assessment and a patient’s natural speech abilities in everyday life
(Lesser and Algar,, 1995; Kim et al.,, 2022; Stark et al.,, 2021; Wallace and
Kimelman,, 2013).

EEG-based event-related potential (ERP) studies have been conducted to address
limitations of behavioral testing. Studies have shown that IWA exhibit altered
ERP components such as the P1, N1, P2, N2, P300, and N400 in response to
language stimuli (Aerts et al.,, 2015; Becker and Reinvang,, 2007; Ilvonen et
al.,, 2001; Ofek et al.,, 2013; Pulvermüller et al.,, 2004; Robson et al.,,
2017). Together with the potential for automatic assessment that requires less
active participation from the patient, these alterations suggest that ERPs may
have diagnostic value in aphasia (Cocquyt et al.,, 2020). Nonetheless, ERP
paradigms involve artificial language stimuli presented repeatedly to the
participant, which questions the ecological validity of the obtained outcomes
(Le et al.,, 2018). Furthermore, previous ERP studies in aphasia have not
reported on the reliability of ERPs, the minimal required recording length and
the sensitivity to capture language impairments at the individual level.

Recent studies have investigated the EEG response to natural, running speech,
which could open new perspectives to studying natural speech processing in
IWA. When listening to speech, the brain tracks the temporal envelope, which
contains essential cues for speech understanding. The envelope consists of the
slow-varying temporal modulations in the speech signal and encompasses cues
for detecting and identifying lexical units (i.e., phonemes, syllables, words
and phrases) and prosody (Peelle and Davis,, 2012). In fact, previous research
showed that listeners can understand speech based on the low-frequency
temporal envelope only (Shannon et al.,, 1995). Neural envelope tracking can
be measured by applying encoding and decoding models on the stimulus and the
recorded EEG. The level of tracking is reflected in the extent to which the
models can either predict the neural signals or decode the envelope. In a
linear (Crosse et al.,, 2021) or a mutual information-based (De Clercq et
al.,, 2023) model, the neural tracking outcomes can be visualized over time
and space (i.e., EEG channels), obtaining response properties similar to
traditional ERP components (Brodbeck et al.,, 2022). The neural tracking
technique is rapidly evolving and has led to crucial new insights as to how
natural speech is processed in the brain. Neural envelope tracking is strongly
related to speech understanding (Ding and Simon,, 2013; Etard and
Reichenbach,, 2019; Kaufeld et al.,, 2020), and can be used to objectively
quantify speech intelligibility (Vanthornhout et al.,, 2018; Gillis et al.,,
2022).

Prior research assigned the low-frequency temporal envelope primarily to
speech understanding. The low-frequency envelope, i.e., delta (0.5–4 Hz) and
theta (4-8 Hz) band, encompass cues for detecting and segmenting lexical
units. The theta band tracks syllables and lower-level acoustic processing of
speech (Etard and Reichenbach,, 2019), while the delta band signal is
associated with processing speech prosody and segmenting higher-level
linguistic structures such as words and phrases (Ding et al.,, 2016; Giraud
and Poeppel,, 2012; Kaufeld et al.,, 2020). In addition to the delta and theta
band, reflecting synthesis of higher-level auditory and linguistic structures,
the alpha and beta bands are involved in attention and auditory-motor coupling
(Wöstmann et al.,, 2017; Fujioka et al.,, 2015), while the gamma band is
involved in encoding phonetic features (Giraud and Poeppel,, 2012; Gross et
al.,, 2013; Hyafil et al.,, 2015). In conclusion, specific frequency bands are
believed to reflect different stages of speech processing.

Neural envelope tracking of natural speech has been investigated in several
clinical populations with language impairments. For individuals with primary
progressive aphasia, a language disorder caused by a neurodegenerative
disease, Dial et al., (2021) reported increased neural tracking in the theta
band but no group differences in the delta band. The researchers argued that
enhanced theta band tracking in individuals with primary progressive aphasia
might reflect a compensation mechanism through increased reliance on acoustic
cues. For individuals with dyslexia, a disorder characterized by phonological
processing difficulties, decreased tracking in delta, theta and beta/gamma
(phoneme- and phonetic-level) band have been reported (Di Liberto and Lalor,,
2017; Lizarazu et al.,, 2021; Mandke et al.,, 2022). In conclusion, these
studies have shown the potential for neural tracking to capture language
impairments in clinical cohorts.

The present study investigated whether we can differentiate IWA in the chronic
phase after stroke (i.e., $\geq$ 6 months post-stroke) from neurologically
healthy, age-matched controls using EEG-based neural envelope tracking.
Specifically, we used mutual information analyses to quantify neural envelope
tracking, which captures linear and nonlinear effects and outperforms linear
models (De Clercq et al.,, 2023). We described both groups’ responses to the
speech envelope temporally and spatially at broadband frequency range. We
further investigated neural tracking in specific frequency bands ranging from
delta to gamma band, as different frequency bands are involved in different
(sub-)lexical processes (Etard and Reichenbach,, 2019; Ding et al.,, 2016;
Giraud and Poeppel,, 2012; Keitel et al.,, 2018; Peelle and Davis,, 2012).

Secondly, we assessed the suitability of the neural tracking technique as a
biomarker to capture language processing difficulties. To this end, we used a
Support Vector Machine (SVM) to classify participants as healthy or aphasic
using MI measures in different frequency bands as input to the model. Finally,
we investigated how much data the neural tracking technique requires for good
classification and reliable outcomes.

##  2 Materials and methods

###  2.1 Participants

Our sample comprised 27 IWA (seven female participants, 73 $\pm$11 y/o) in the
chronic phase ($\geq$ 6 months) after stroke and 22 neurologically healthy
controls (seven female participants, 72 $\pm$7 y/o). There was no significant
age difference between groups (unpaired Wilcoxon rank sum test: W=343.5,
p=0.36). IWA were recruited at the stroke unit of the University Hospital
Leuven and via speech-language pathologists. Healthy controls were recruited,
making sure they matched the age of IWA at the group level. The inclusion
criteria for IWA were: (1) a left-hemispheric or bilateral stroke, (2) a
diagnosis of aphasia in the acute stage after stroke using behavioral language
tests and (3) no formal diagnosis of a psychiatric or neurodegenerative
disorder. A detailed overview including demographic information (age, sex,
time since stroke, speech-language therapy, performance on diagnostic tests
for aphasia at time of participation) and lesion information (stroke type,
affected blood vessel, lesioned hemisphere) about the aphasia sample can be
found in the Supplementary materials (Supplementary Table 1). For more
information regarding recruitment strategy and diagnosis in the acute stage
after stroke, we refer to Kries et al., (2022). The study was approved by the
ethical committee UZ/KU Leuven (S60007), and all participants gave written
consent before participation. Research was conducted in accordance with the
principles embodied in the Declaration of Helsinki and in accordance with
local statutory requirements.

Participants completed standardized clinical tests for aphasia at the time of
participation as described in detail in Kries et al., (2022). IWA scored
significantly lower on the ’Nederlandse Benoemtest’, i.e., Dutch Naming Test
(Van Ewijk et al.,, 2020), and the ScreeLing test (El Hachioui et al.,, 2017;
Visch-Brink et al.,, 2010) compared to healthy controls (W=57.5, p<0.001;
W=101, p<0.001, respectively). Although seven IWA did not score below the cut-
off threshold for aphasia on either of these tasks, they were still attending
speech-language therapy sessions at the time of participation and had extended
documentation of language deficits in the acute stage after stroke (Kries et
al.,, 2022).

###  2.2 EEG experiment

The EEG measurements took place in a soundproof, electromagnetically shielded
booth using a 64-channel BioSemi ActiveTwo system (Amsterdam, the Netherlands)
at a sampling frequency of 8,192 Hz. Participants were instructed to listen to
a 25-minute-long story, De Wilde Zwanen, written by Christian Andersen and
narrated by a female Flemish-native speaker, presented in silence while their
EEG was recorded. The story was cut into five parts with an average duration
of 4.84 minutes. After each story part, participants answered content
questions about the preceding part, introduced to make the participant follow
the content attentively. As one participant fell asleep during two story
parts, we decided to leave these parts out of the analysis for that
participant (sub-019, Supplementary Table 1). Participants had a short break
after each story part and answered content questions about the preceding part.
The protocol introduced these questions to make participants follow the story
attentively. The story was presented bilaterally through ER-3A insert
earphones (Etymotic Research Inc, IL, USA) using the software platform APEX
(Francart et al.,, 2008).

We determined a subject-dependent intensity level at which the story was
presented based on the thresholds of the pitch tone audiometry (PTA). We
defined hearing thresholds for octave frequencies between .25 and 4 kHz. For
normal hearing participants, the story was presented at 60 dBA. For hearing
impaired participants, defined as participants that have a hearing threshold
>25 dB hearing loss on frequencies below 4 kHz, the volume was augmented with
half of the pure tone average of the individual thresholds at .25, .5 and 1
kHz for both ears individually. This procedure was adapted from Jansen et al.,
(2012). To check whether age-related hearing loss differed between both
groups, we calculated the Fletcher index, i.e., average of PTA thresholds at
.5, 1 and 2 kHz. Hearing levels did not differ between groups (Fletcher index
averaged across the right and left ear: W=326.5, p=0.56).

###  2.3 Signal processing

#### Envelope extraction

We used a gammatone filter bank (Søndergaard et al.,, 2012) to extract the
envelope. We used 28 channels spaced by one equivalent rectangular bandwidth
and center frequencies from 50 Hz until 5000 Hz. The envelopes were extracted
from each sub-band by taking the absolute value of each sample and raising it
to the power of 0.6. The resulting 28 sub-band envelopes were averaged to
obtain a single envelope. Next, the envelope was downsampled to 512 Hz to
decrease processing time. The envelope was then filtered in frequency ranges
of interest. These include delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-12 Hz),
beta (12-30 Hz), low-gamma (30-49 Hz) and a broad (0.5-49 Hz, including all
individual frequency ranges) band. We used high- and lowpass filters, with a
transition band of 10$\%$ below the highpass and 10$\%$ above the lowpass
frequency. A Least Squares filter of order 2000 was used, and we compensated
for the group delay. After filtering, the envelope was normalized and further
downsampled to 128 Hz.

#### EEG data processing

EEG data were pre-processed using the Automagic toolbox (Pedroni et al.,,
2019) and custom Matlab scripts (The MathWorks Inc., Natick, MA, USA, 2021).
The EEG signals were first downsampled to 512 Hz to decrease processing time.
Artifacts were removed using the artifact subspace reconstruction method
(Mullen et al.,, 2015). Next, an independent component analysis was applied to
the data, and components classified as "brain" or "other" (i.e., mixed
components), using the EEGLAB plugin ICLabel (Pion-Tonachini et al.,, 2019),
with a probability higher than 50$\%$ were preserved (average number of
removed components: 26 $\pm$7). The neural signals were projected back to the
channel space, where the signals were average referenced. Subsequently, we
filtered the EEG data in the same frequency bands using the same Least Squares
filter as in the envelope extraction method. Next, normalization and further
downsampling to 128 Hz were applied.

###  2.4 Neural envelope tracking

We investigated neural envelope tracking using the Gaussian copula MI analysis
(Ince et al.,, 2017). In the Gaussian copula approach, all variables (the
envelope and EEG channels) are first ranked on a scale from 0 to 1, obtaining
the cumulative density function (CDF). By computing the inverse standard
normal CDF, the data distributions of all variables are transformed to perfect
standard Gaussians. Subsequently, the parametric Gaussian MI estimate can be
applied to the data provided by:

$I(X;Y)=\frac{1}{2ln2}\Biggl{[}\frac{|\sum_{X}||\sum_{Y}|}{|\sum_{XY}|}\Biggr{]}$
(1)

where I(X;Y) equals the MI between X and Y (here, the EEG and the envelope),
expressed in bits. $|\sum_{X}|$ and $|\sum_{Y}|$ are the determinants of the
covariance matrices of $X$ and $Y$, and $|\sum_{XY}|$ is the determinant of
the covariance matrix for the joint variable. To obtain temporal information
on MI, we shifted the EEG as a function of the envelope over time (using an
integration window -200 to 500 ms) and applied Eq. (1) at each sample. The
result forms the temporal mutual information function (TMIF) and reflects how
the brain processes speech over time (De Clercq et al.,, 2023; Zan et al.,,
2020). For an in-depth explanation of the Gaussian copula MI method, we refer
to Ince et al., (2017). For a more practical explanation of the TMIF in the
context of neural envelope tracking, see De Clercq et al., (2023).

We calculated the single-channel TMIF and the multivariate TMIF, analog to a
(linear) encoding and decoding model, respectively. The single-channel TMIF
calculates the TMIF for each channel individually, providing both temporal
(i.e., peak latency and peak magnitude) and spatial (i.e., topography)
information on speech processing. Alternatively, the multivariate TMIF
determines the multivariate relationship between multiple EEG channels
combined and the speech envelope. This latter method is statistically more
powerful as it takes interactions between EEG channels into account. However,
it is restricted to temporal interpretations only. For the multivariate TMIF,
we used a channel selection including fronto-central and parieto-occipital
channels that contribute to speech processing (Lesenfants et al.,, 2019). Our
channel selection is visualized in Supplementary Fig. 1.

#### Permutation testing

Neural tracking (MI in bits, in this case) is a relative metric and should be
compared to a null-distribution to quantify the meaningfulness of the derived
values (De Clercq et al.,, 2023). We created stationary noise that matched the
spectrum of the envelope per frequency band individually. Next, we calculated
the MI between the noise envelope and the EEG per participant and repeated
this process 1000 times. The significance level was then determined as the
95th percentile of permutations per participant (resulting in a single
significance level per participant).

No significant differences were found in the significance level between IWA
and controls for any frequency band, as determined by Wilcoxon rank sum tests.
Supplementary Fig. 2 displays the significance levels of the multivariate TMIF
for all frequency bands categorized by group. As no significant differences in
the significance level were found between groups, we used a single
significance level (i.e., the 95th percentile of permutations across all
participants) to interpret the multivariate TMIFs in the Results section.

###  2.5 Statistics

#### Group comparisons

We compared neural envelope tracking for IWA with the control group for
broadband as well as for delta, theta, alpha, beta and gamma frequency ranges.
For the single-channel TMIF, we performed non-parametric spatio-temporal
cluster-based permutation tests (Maris and Oostenveld,, 2007), indicating
clusters in the TMIF over time and space with the largest group difference at
threshold p<0.05. For the multivariate TMIF, we performed non-parametric
temporal cluster-based permutation tests (Maris and Oostenveld,, 2007),
indicating clusters of samples with the largest group difference at threshold
p<0.05.

#### Support Vector Machine Classification

We investigated whether EEG-based envelope tracking outcomes can be used for
detecting aphasia. To this end, we used a Support Vector Machine (SVM) to
classify held-out participants as control or aphasic using the Scikit-Learn
(v. 0.24.2) library in Python (Pedregosa et al.,, 2011). The multivariate
TMIFs for all five individual frequency bands (delta to gamma) were used as
input to the model. Additionally, we added age of the participant, as it
influences neural envelope tracking (Decruy et al.,, 2019). We chose a radial
basis function kernel SVM and performed a nested cross-validation approach. In
the inner cross-validation, the C-hyperparameter and pruning (i.e., length of
the TMIFs) were optimized (accuracy-based) and tested in a validation set
using 5-fold cross-validation. The trained model was then tested on the test
set, for which we used a leave-one-subject-out cross-validation approach.

The performance of the SVM classifier was evaluated by computing the receiver
operating characteristic (ROC) curve and calculating the area under the curve
(AUC). We further reported the overall accuracy, the F1-score, the sensitivity
and the specificity of the classifier.

Feature contribution. To obtain a proxy for the relevant contribution of each
frequency band, we left out a single band and re-fitted the SVM. We repeated
this process for all five frequency bands and reported the corresponding
performance drop (AUC, accuracy, F1-score).

#### Recording time

Classification. From a practical perspective, we were interested in how much
data the neural envelope tracking technique requires to detect aphasia
accurately and obtain stable, reliable results. We iteratively cropped the EEG
recording and the envelope in steps of 2 minutes (using the first 1 minute,
first 3 minutes, 5, 7… up to the entire 25 minutes of recording time) and
calculated the TMIF per frequency band per time duration. Next, we
investigated the amount of minutes required for the SVM to reach its
classification potential. As described above, we trained and tested our SVM
per time duration in the same fashion as the entire duration. Performance
(AUC, accuracy, F1-score) was plotted as a function of recording time. We
determined the knee point, i.e., the point at which the performance benefit
starts to saturate, using the "kneed" python package (Satopaa et al.,, 2011).
The knee point of this curve reflects the point at which the increase in model
performance may no longer be worth the corresponding effort.

Within-subjects stability. Second, we investigated the data required to obtain
stable, reliable results. We determined the within- and between-subjects
stability per time duration. For the within-subjects stability, we
individually correlated (Pearson) the TMIF per time duration (i.e., first
minute, first 3, 5, …) with the TMIF of the entire recording per subject. This
resulted in a single correlation coefficient for each participant, frequency
band and time duration. Next, all correlations were plotted as a function of
recording time, and we determined the knee point of the curve on the average
across all frequency bands. As such, we gained insight into the amount of data
required for a participant’s TMIF to become stable (i.e., when there is not
much change in an individual’s TMIF).

Between-subjects stability. For the between-subjects stability, we calculated
each participant’s mean MI of the TMIF (integration window 0-400 ms) per time
duration (1, 3, 5,… minutes) and the entire recording. This resulted in a
single datapoint per participant, frequency band and time duration (i.e., mean
MI for a certain duration length x mean MI entire duration). Subsequently, we
calculated the correlation coefficient (Pearson’s R) between the mean MI for
certain time duration and frequency band with the entire recording over
participants on the group level, resulting in a single correlation coefficient
per time duration and frequency band. We plotted the correlations as a
function of recording time and determined the knee point of the curve on the
average across all frequency bands. With this analysis, we investigated the
amount of data required for a participant’s relative (i.e., compared to other
participants) strength of tracking to become stable (i.e., from which point on
a participant’s relative neural tracking compared to other participants is no
longer expected to change).

#### Split-half reliability

Finally, we report a traditional split-half reliability metric with non-
overlapping parts of the recording. We split the EEG recording into two equal
parts, i.e., the first 12.5 minutes and the second 12.5 minutes, and computed
the TMIFs for each half and each frequency band individually. Next, we
computed the mean MI value of the TMIF (0-400 ms) for the first and the second
half of the recording per participant individually. Subsequently, we
calculated the correlation coefficient (Pearson’s R) between the first and
second half of the recording over participants on the group level (Pearson’s
R) for IWA and controls separately.

### Data availability statement

We shared our neural tracking outcomes (i.e., the TMIFs) on the Open Science
Framework: https://osf.io/nkmfa/. Note that our ethical approval does not
permit public archiving of raw neuroimaging data, but raw EEG data can be made
available upon request and if the GDPR-related conditions are met.

##  3 Results

###  3.1 Distinguishing individuals with aphasia from healthy controls

We investigated whether neural envelope tracking is altered in IWA compared to
healthy controls. First, we studied the effect in the broadband frequency
range (0.5-49 Hz). For the single-channel MI analysis, providing both temporal
and spatial information, we found decreased neural envelope tracking for IWA
compared to healthy controls (Fig. 1A). A spatio-temporal cluster-based
permutation test identified a cluster comprising a large group of fronto-
central, parietal and posterior channels (N = 43 channels) from 0.11 s to 0.3
s (p=0.004), centered around the second peak. The multivariate MI analysis,
which combines information from multiple channels, confirmed these results: a
temporal cluster-based permutation test identified a cluster between 0.11 s to
0.26 s in which IWA displayed a decreased response (p = 0.005) (Fig. 1B).

Figure 1: Broadband analysis. A. The average single-channel TMIF for the
control and the aphasia group separately, with topoplots at the first and
second peak (0.05 and 0.17 s). The spatio-temporal cluster-based permutation
test investigated the difference between the control and aphasia group
(control - aphasia) and identified a cluster (below threshold p<0.05) with the
largest group difference, centered around the second peak. Brain latencies
belonging to the cluster are marked in a shaded gray area, the channels
belonging to the cluster are indicated with a black dot on the topoplot. B.
The group average TMIF, for both groups separately. The shaded, colored areas
indicate the 95% confidence interval. The shaded gray area indicates the
cluster with largest group difference (threshold p<0.05), identified using a
temporal cluster-based permutation test. ** = p<0.01

We further investigated the neural response in narrow frequency bands. We
focused on the multivariate TMIF as it is a statistically more robust method
compared to the single-channel TMIF, and we used those features as input to
our SVM classifier in the subsequent section. The single-channel TMIFs for all
frequency bands are provided in the Supplementary materials. We generally
observed decreased neural envelope tracking for IWA compared to healthy
controls (Fig. 2). Temporal cluster-based permutation tests identified
clusters below threshold p<0.05 for delta (0.1 to 0.30s, p=0.003), theta (0.04
to 0.27s, p=0.005) and gamma (0.01 to 0.1s, p = 0.004) band. No clusters
exceeding the p<0.05 threshold were detected for the alpha and beta bands.
These results are confirmed in the single-channel MI analysis, where spatio-
temporal cluster-based permutation tests identified clusters for delta, theta
and gamma band for a large group of fronto-central, parietal and posterior
channels (visualizations and statistics provided in the Supplementary
materials).

Figure 2: Frequency-specific analysis. Group average TMIF’s visualized per
frequency band, with colored shaded areas indicating the 95% confidence
interval. Shaded, gray areas indicate clusters with largest group difference
(below threshold p<0.05) identified using temporal cluster-based permutation
tests. ** = p<0.01

###  3.2 Support Vector Machine classification

Next, we investigated whether we could detect aphasia based on neural envelope
tracking measures in the individual frequency bands. To this end, we used an
SVM to classify participants as belonging to the aphasia or the healthy
control group via leave-one-out cross-validation. We used the TMIFs in our
five frequency bands of interest and age as input features to the model. The
SVM successfully classified participants belonging to either group with an
accuracy of 83.67%, an F1-score of 83.58% and an AUC of 88.05%. The SVM had a
sensitivity of 88.89% and a specificity of 77.27% for aphasia. Fig. 3A
displays the ROC curve.

Figure 3: Result of the SVM classifier. A. Receiver operating characteristic
curve (ROC). B. Relative feature importances. Drop in performance is
visualized after leaving out the corresponding feature (i.e., frequency band).

To obtain a measure of relative frequency band contribution, we iteratively
left out a frequency band and trained the SVM with the remaining features. For
each left-out frequency band, we calculated the performance drop. As assessed
with accuracy and F1-score metrics, theta, followed by delta, gamma, alpha and
beta caused the largest drop in performance (see Fig. 3B). When estimated with
AUC, the accuracy was still the highest for theta, followed by gamma, delta,
alpha and beta. This confirmed our group comparison analyses: delta, theta and
gamma band are the most relevant, discriminating features.

###  3.3 Recording length

We further investigated how much data the neural envelope tracking technique
requires for robust and stable results (Fig. 4). With only one minute of
recording time, the SVM obtained classification accuracy close to chance-level
(55%). Yet, from 5 minutes on, the SVM reached an accuracy of 81.63%, and
performance fluctuated between 81.63% and 85.71% for the remaining part of the
recording. In practice, this corresponds to one less or one additional
correctly classified participant with respect to the full recording (Fig. 3A).
The knee point of the curve was identified at 5 minutes of recording length.
From 9 minutes on, the SVM converged to an AUC of 80%. However, compared to
the entire recording length (AUC=88.05%), its full potential is reached from
13 minutes on (AUC robustly crossed 85%, with a maximum of 89.73% at 15
minutes). The F1-score mostly overlapped with the accuracy and never differed
more than 0.44% . The SVM performance is plotted as a function of recording
length, displayed in Fig. 4A.

Figure 4: Recording Length.  A. Performance (accuracy, F1-score, AUC) of the
SVM classifier plotted as a function of time. B. Within-subjects stability for
all five frequency bands and the average across frequency bands (black line).
Shaded areas indicate the standard error. C. Between-subjects stability for
all five frequency bands and the average across frequency bands (black line).
The knee point of all panels is indicated with a vertical dotted line (based
on the average for panels B and C).

The within-and between-subjects stability is plotted as a function of
recording time in Fig. 4B and 4C. Highest within- and between-subjects
correlations were observed for the low-frequency bands, namely delta and
theta. Taking the average of all frequency bands, we identified the curve’s
knee point at 7 minutes of recording length (see black dotted lines). The
within-subjects stability (Fig. 1 4B) had an average correlation of R=0.73,
and the between-subjects stability (Fig. 4C) had an average correlation of
R=0.79 at the knee point of the curve.

###  3.4 Reliability of neural envelope tracking

Finally, we calculated the split-half reliability of neural envelope tracking.
Table 1 provides the correlations and statistics. We generally found higher
correlations in the lower frequency bands (delta and theta). Correlations were
comparable between IWA and the control group; the 95% confidence intervals
overlapped for each frequency band, and post-hoc Fisher z-tests, performed
using the ’cocor’ package in Rstudio (Diedenhofen and Musch,, 2015), revealed
no difference in correlation strength between both groups.

Table 1: Split-half reliability.

| delta | theta | alpha | beta | gamma
---|---|---|---|---|---
| IWA | C | IWA | C | IWA | C | IWA | C | IWA | C
Pearson’s R | 0.85 | 0.91 | 0.96 | 0.92 | 0.75 | 0.88 | 0.61 | 0.62 | 0.62 | 0.73
CI | [0.68 ; 0.93] | [0.78 ; 0.96] | [0.91 ; 0.98] | [0.82 ; 0.96] | [0.52 ; 0.88] | [0.72 ; 0.95] | [0.31 ; 0.81] | [0.27 ; 0.83] | [0.31 ; 0.81] | [0.43 ; 0.88]
p-value | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | 0.006 | 0.02 | 0.007 | 0.002
Fisher’s z (p-value) | z=0.85 (1) | z=-0.94 (1) | z=1.24 (1) | z=0.04 (1) | z=0.66 (1)

CI=95% confidence interval; C=Control group. Fisher z-test comparing controls
- IWA. All p-values are corrected for multiple comparisons.

##  4 Discussion

We conducted an in-depth study on neural envelope tracking of natural speech
in post-stroke aphasia. First, we found that IWA display decreased neural
envelope tracking compared to heatlhy controls for a broadband frequency
range. Second, frequency-specific analyses indicated that group differences
are most prominent in the delta, theta and gamma frequency ranges. Third, the
suitability of neural envelope tracking measures as a biomarker for post-
stroke aphasia was demonstrated using an SVM classifier which yielded high
accuracy (84%, AUC 88%). Finally, we showed that an assessment based on neural
envelope tracking could be obtained in a time-efficient (5 minutes of EEG
recording) and highly reliable manner.

###  4.1 Individuals with aphasia display decreased neural envelope tracking

#### Broadband frequency analysis

Neural envelope tracking at broadband is decreased in IWA compared to healthy
controls. The single-channel TMIF analysis revealed a cluster at neural
response latencies centered around the second peak in the TMIF comprising a
large group of fronto-central, temporal and parieto-occipital channels (see
Fig. 1A). The multivariate TMIF confirmed this result: a temporal cluster
comprising brain latencies surrounding the second peak in the TMIF (Fig. 1B)
was identified. A recent neural tracking study showed that the second peak
emerges when speech is comprehensible and diminishes when it is not
understood. By contrast, the first peak displayed a prominent response when
speech was incomprehensible (Verschueren et al.,, 2022). Thus, the second peak
we observed here is most likely related to speech understanding, while the
first peak is likely more implicated in acoustically processing the signal.
Therefore, it is not surprising that in IWA, where language understanding is
impaired, the second peak in the TMIF is decreased compared to healthy
controls.

#### Frequency-specific analysis

We further investigated neural envelope tracking in narrow frequency bands.
Our findings revealed a decrease in tracking for IWA compared to healthy
controls in the low-frequency bands (delta and theta, see Fig. 2A and 2B),
which are crucial for speech understanding (Vanthornhout et al.,, 2018). The
delta band encodes sentences, phrases and words (Kaufeld et al.,, 2020; Keitel
et al.,, 2018), while theta band tracks the syllable rate of the stimulus
(Etard and Reichenbach,, 2019; Lizarazu et al.,, 2019). Neural tracking in the
low-frequency bands drops when these linguistic units become incomprehensible
(Kaufeld et al.,, 2020; Keitel et al.,, 2018; Xu et al.,, 2022). Atypical
neural tracking of the low-frequency temporal envelope has been reported in
several clinical populations, including individuals with primary progressive
aphasia (Dial et al.,, 2021) and dyslexia (Di Liberto and Lalor,, 2017;
Lizarazu et al.,, 2021; Mandke et al.,, 2022). In the case of dyslexia, which
is characterized by phonological processing difficulties, alterations in low-
frequency envelope tracking are believed to reflect an atypical sampling
mechanism that affects faster modulations at the phoneme and grapheme level
(Mandke et al.,, 2022). These findings in healthy and clinical populations
demonstrate the potential of neural tracking measures of the low-frequency
envelope as a biomarker for language impairments.

Fewer studies investigated the role of high-frequency neural envelope
tracking. Some studies suggest a role for alpha and beta in attention and
auditory-motor coupling (Wöstmann et al.,, 2017; Fujioka et al.,, 2015), and
for the gamma band in encoding phonetic features (Hyafil et al.,, 2015; Giraud
and Poeppel,, 2012; Gross et al.,, 2013). Our study found no group differences
in the alpha and beta bands. However, individuals with aphasia displayed
decreased neural envelope tracking in the gamma band. The neural response in
the gamma band was characterized by an early response peak (Fig. 2) and a
group difference present in the right hemisphere (see Supplementary Fig. 7).
This early response latency in the gamma band aligns with the idea of a linear
phase property, where the neural response delay in higher frequency bands is
shorter (Zou et al.,, 2021). A somewhat similar neural response pattern
characterized by an early response latency and a right hemisphere bias in the
high gamma band (>70 Hz) was also reported by Kulasingham et al., (2020). The
gamma band has been of particular interest in dyslexia research, with several
studies reporting alterations in gamma band activity. However, most studies
have focused on phase-locking and phase coherence in response to amplitude-
modulated noise (for a review, see (Lizarazu et al.,, 2021)), while few have
investigated gamma band neural envelope tracking during natural speech
processing (Mandke et al.,, 2022). To gain a better understanding of
(low-)gamma band neural envelope tracking of natural speech, which we have
shown to demonstrate robust group differences and contribute significantly to
detecting aphasia (as depicted in Fig. 3B), future research should aim to
further investigate its implications for speech understanding and language
impairments.

In line with the idea that individual frequency bands are involved in
different speech processes, exploratory analysis revealed low to moderate
positive and negative correlations between neural tracking in individual
frequency bands (see Supplementary Table 2). This suggests that a participant
with high neural tracking in one frequency band may not necessarily display
high neural tracking in other frequency bands. In contrast, a broadband
frequency analysis shows high redundancy compared to the delta band (R=0.79
for IWA, R=0.92 for controls), which can be attributed to the fact that most
power in the EEG and the envelope is concentrated in the lowest frequencies.
This highlights the relevance of conducting frequency-specific analyses. We
believe that the use of frequency-specific features to train the SVM favored
good classification results, as discussed in the next section. Future research
should investigate whether the neural response to these frequency bands may
capture specific language deficits.

###  4.2 High accuracy detection of post-stroke aphasia

We assessed the suitability of the neural tracking technique to detect post-
stroke aphasia using an SVM classifier with the TMIFs computed in the
individual frequency bands as input to the model. The SVM robustly detected
aphasia, with an accuracy of 83.67%, an F1-score of 83.58% and an AUC of
88.05%. The ROC curve, plotting the true positive as a function of false
positive aphasia classification, is depicted in Fig. 3A. The relative
contribution of individual frequency bands for detecting aphasia at the
individual level confirmed our group comparison results: delta, theta and
gamma band neural tracking were most predictive for capturing aphasia (see
Fig. 3B).

These performance outcomes of the SVM can be interpreted against behavioral
assessment. As described in the Methods section, 7 out of 27 IWA (i.e., 26%)
did not score below the cut-off threshold on either of the two diagnostic
language tests for aphasia administered during the study El Hachioui et al.,
(2017); Van Ewijk et al., (2020). Nevertheless, these subjects had extended
language deficit documentation and followed speech-language therapy at the
time of participation. Although a more extensive screening for aphasia could
have identified a language deficit, this finding highlights the challenge of
detecting aphasia in the chronic phase following a stroke. While further
investigation is required, the higher detection accuracy of the EEG-based
neural tracking classification suggests that it may be more sensitive than
behavioral screening tests for capturing subtle language problems in
individuals with aphasia.

Nonetheless, comparing the SVM classification accuracy to behavioral
assessment is rather difficult, as the underlying tested language skills are
different. Standardized aphasia tests use isolated sounds, phonemes, words or
short sentences, questioning the ecological validity of such tasks (Hamilton
and Huth,, 2018). Consequently, research reports a discrepancy between common
test outcomes and everyday life speech assessments (Lesser and Algar,, 1995;
Kim et al.,, 2022; Stark et al.,, 2021; Wallace and Kimelman,, 2013), and
cognitive problems can bias the test result (Fonseca et al.,, 2019; Rohde et
al.,, 2018). While there exist behavioral natural speech assessments
(Armstrong,, 2000), they are only limitedly applied in practice due to the
high workload (time-intensive) and a lack of knowledge of natural speech
analyses (Bryant et al.,, 2019; Stark et al.,, 2021). Novel automatic speech
recognition and natural language processing techniques (Dalton et al.,, 2022;
Jamal et al.,, 2017; Le et al.,, 2018) may provide a solution in the future.
The neural tracking technique directly addresses the limitation of low
ecological validity from which behavioral tests suffer.

###  4.3 Assessing time-efficiency, stability and reliability

We investigated how much data neural envelope tracking requires to detect
aphasia accurately and yield reliable results. We assessed SVM classification
performance as a function of recording time, as shown in Fig. 4A. Our findings
indicate that high-accuracy detection can be achieved with just 5 minutes of
recording time (accuracy of 81.63%). However, extending the recording duration
to 13 minutes can provide additional benefits in terms of the AUC, with a
robust increase above 85%, and a maximum AUC of 89.73% achieved at 15 minutes.
In summary, our results demonstrate that neural envelope tracking can
effectively detect aphasia in a time-efficient manner, consistent with prior
recommendations that language assessments in aphasia should not exceed 15
minutes to avoid fatigue and cognitive/attentional challenges (El Hachioui et
al.,, 2014). Our findings have important implications for potential clinical
applications of neural envelope tracking in aphasia.

We further investigated the amount of data necessary for our frequency band
features to achieve stability. Our within- and between-subjects stability
analysis revealed that 7 minutes recording length is sufficient for the TMIF
of individual subjects and at the group-level to resemble the TMIF from the
entire recording (see Fig. 4B and 4C). These findings were consistent within
both groups, with 7 minutes being the minimum recording length required (as
illustrated in Supplementary Fig. 8). Notably, we observed that lower
frequency bands (delta and theta) converge more rapidly compared to higher
frequency bands. Within 3-5 minutes, stability correlations for these bands
were relatively high, robustly crossing R=0.80. In contrast, higher frequency
bands require a longer time to converge and exhibit a more linear slope
compared to the delta and theta bands. These less stable results and longer
minimal recording length for the higher frequency bands can be attributed to
their lower signal-to-noise ratio.

To summarize, our results indicate that 5-7 minutes of recording time are
sufficient for assessing neural envelope tracking at low-frequency ranges,
which reflect higher-level linguistic processes and speech understanding.
However, a more comprehensive evaluation that includes higher frequency bands,
which can provide minor additional benefits, requires a slightly longer
recording duration (>13 minutes). These findings are consistent with previous
research in healthy participants, which suggests that low-frequency neural
tracking requires approximately 3-10 minutes of recording time for robust
outcomes (Desai et al.,, 2023; Di Liberto and Lalor,, 2017; Mesik and
Wojtczak,, 2022). Our study contributes an innovative approach by defining the
minimal recording length required to detect language impairments at the
individual level and suggests that the recording duration for future studies
in individuals with language impairments may depend on the specific research
question being addressed.

Previous studies on aphasia using ERPs have suggested the potential of this
approach for clinical diagnosis, but without reporting on its reliability
(Cocquyt et al.,, 2020). However, evaluating the reliability of test results
is crucial to determine the usefulness of capturing individual language
impairments. In this study, we assessed the reliability of neural envelope
tracking using split-half reliability metrics. The results demonstrate strong
correlations between both halves, particularly in the delta and theta bands
(Table 1). Our findings are consistent with previous research reporting a
correlation of R=0.89 for delta and R=0.82 for theta across two stories in a
cohort with language impairments caused by a neurodegenerative disorder (Dial
et al.,, 2021). Yet, reliability measures in our study were generally lower
for higher frequency bands. As mentioned earlier, these bands require more
data to converge and have a lower signal-to-noise ratio. It is worth noting
that our reliability measure may be affected by fatigue. Thus, future studies
should examine the generalizability of the results across stories and speakers
at different sessions (i.e., test-retest) to further investigate the
reliability of neural tracking for applications in aphasia.

###  4.4 Limitations and future directions

Our study demonstrates that neural envelope tracking is a reliable and
accurate method for detecting language impairments in aphasia. However, our
current approach does not provide information on the specific language profile
of the patient (i.e., which underlying language component, e.g., auditory,
phonetic, semantic,… is affected). Investigating these deficits would require
a larger sample size with a more uniform spread of aphasia severity levels. In
future research, we suggest exploring whether neural tracking in specific
frequency bands can cluster different language profiles in aphasia. In
addition, recent studies investigated the neural response to speech
representations beyond the temporal envelope. For example, it has been shown
that linguistic speech representations at phoneme and word level can improve
the model’s fit to the EEG (Di Liberto et al.,, 2015; Gillis et al.,, 2021)
and can provide complementary information on speech processing (Verschueren et
al.,, 2022; Gillis, Kries et al.,, 2023). Future research should (1) examine
whether incorporating these linguistic speech representations can enhance
aphasia detection and inform on specific language deficits and (2) assess the
reliability and robustness of these features, which is currently lacking in
the literature.

Several other open questions must be addressed before neural tracking can be
applied in clinical settings. Firstly, neural tracking must be applied to IWA
in the acute stage after stroke. This work considered the chronic stage only
since it is characterized by a more stable language profile (Johnson et al.,,
2019). Secondly, the present study distinguished IWA and healthy controls
only. If neural tracking is to be used for screening aphasia in the acute
stage post-stroke, a clear dissociation between stroke patients with and
without aphasia is crucial. However, such dissociation is generally not
considered in behavioral screening tests despite being used in the clinic on a
daily basis (Rohde et al.,, 2018). Lastly, this study used language stimuli in
the receptive domain only. Recent studies have suggested that the same
analysis can also be applied to the expressive domain, i.e., speech production
(Perez et al.,, 2022), which could open new perspectives to studying
expressive language problems in IWA.

### Conclusion

This study investigated neural envelope tracking of natural speech in patients
with chronic post-stroke aphasia. The findings showed that individuals with
aphasia exhibited reduced brain responses in the delta, theta, and gamma
bands, likely reflecting decreased processing of higher-level auditory and
linguistic units. The study also demonstrated the efficacy of neural tracking
in capturing language impairments at the individual level in a highly reliable
and time-efficient manner, which suggests its promising clinical potential as
an assessment tool. Despite these positive results, several open questions
remain that need to be addressed before neural tracking can be used in
clinical settings. For instance, it remains unclear whether neural tracking
can accurately capture specific language problems, and its effectiveness in
assessing patients in the acute stage post-stroke requires future
investigation. Nevertheless, our work represents a significant step towards
more automatic and ecologically valid assessments of language problems in
aphasia.

## Acknowledgements

The authors would like to express their heartfelt gratitude to all the
participants, particularly those with aphasia and their families that
supported them. We would also like to extend our thanks to Dr. Klara
Schevenels for her assistance in the recruitment process, as well as the
individuals that helped with the data collection: Janne Segers, Rosanne
Partoens, Charlotte Rommel, Ines Robberechts, Laura Van Den Bergh, Anke
Heremans, Frauke De Vis, Mouna Vanlommel, Naomi Pollet, Kaat Schroeven, Pia
Reynaert and Merel Dillen.

## Funding

Research of Pieter De Clercq was supported by the Research Foundation Flanders
(FWO; PhD grant 1S40122N). Jill kries was financially supported by the
Luxembourg National Research Fund (FNR; AFR-PhD project reference 13513810).
Research of Jonas Vanthornhout was supported by FWO (postdoctoral grant:
1290821N). The presented study further received funding from the European
Research Council (ERC) under the European Union’s Horizon 2020 research and
innovation programme (Tom Francart; grant agreement No. 637424), and by the
FWO grant No. G0D8520N.

## Competing interests

The authors declare no conflicts of interest, financial or otherwise.

## Supplementary material

### Channel Selection

Supplementary Fig. 1: Channel selection.

### Significance level of neural envelope tracking

Supplementary Fig. 2: Significance level of neural tracking. Boxes represent
the 95th percentile of permutations per subject and per frequency band. There
was no significant difference between groups for any frequency band.

### Single-channel TMIF analysis

#### Delta band

The single-channel TMIF analysis revealed decreased delta band envelope
tracking for IWA compared to healthy controls. A spatio-temporal cluster-based
permutation test identified a cluster (p=0.005) comprising a large group of
bilateral fronto-central, parietal and posterior channels (N = 46 channels)
and brain latencies from 0.09 s to 0.5 s. The results are depicted in Fig. 3.

Supplementary Fig. 3: Delta band analysis. The average single-channel TMIF in
delta band for the control and the aphasia group separately, with topoplots at
indicated brain latencies. The spatio-temporal cluster-based permutation test
investigated the difference between the control and aphasia group (control \-
aphasia) and identified a cluster (below threshold p<0.05) with the largest
group difference. Brain latencies belonging to the cluster are marked in a
shaded gray area, the channels belonging to the cluster are indicated with a
black dot on the topoplot.** = p<0.01

#### Theta band

For the theta band, the single-channel TMIF analysis revealed decreased
envelope tracking for IWA compared to healthy controls. A spatio-temporal
cluster-based permutation test identified a cluster (p=0.005) comprising a
large group of bilateral fronto-central, parietal and posterior channels (N =
40 channels) and brain latencies from 0.09 s to 0.31 s. Fig. 4 visualizes the
result.

Supplementary Fig. 4: Theta band analysis. The average single-channel TMIF in
theta band for the control and the aphasia group separately, with topoplots at
indicated brain latencies. The spatio-temporal cluster-based permutation test
investigated the difference between the control and aphasia group (control -
aphasia) and identified a cluster (below threshold p<0.05) with the largest
group difference. Brain latencies belonging to the cluster are marked in a
shaded gray area, the channels belonging to the cluster are indicated with a
black dot on the topoplot.** = p<0.01

#### Alpha band

In the alpha band, a spatio-temporal cluster-based permutation test found no
clusters exceeding p<0.05 threshold level. The group results are displayed in
Fig. 5.

Supplementary Fig. 5: Alpha band analysis. The average single-channel TMIF in
alpha band for the control and the aphasia group separately, with topoplots at
indicated brain latencies. The spatio-temporal cluster-based permutation test
investigated the difference between the control and aphasia group (control -
aphasia), but did not find a group difference with p-value below threshold
level 0.05.

#### Beta band

In the beta band, a spatio-temporal cluster-based permutation test found no
clusters exceeding p<0.05 threshold level. The group results are displayed in
Fig. 6.

Supplementary Fig. 6: Beta band analysis. The average single-channel TMIF in
beta band for the control and the aphasia group separately, with topoplots at
indicated brain latencies. The spatio-temporal cluster-based permutation test
investigated the difference between the control and aphasia group (control -
aphasia), but did not find a group difference with p-value below threshold
level 0.05.

#### Gamma band

Finally, IWA displayed decreased neural envelope tracking in the gamma band. A
spatio-temporal cluster-based permutation test identified a cluster (p=0.03)
comprising parietal and posterior channels (N = 12 channels), primarily in the
right hemisphere, and brain latencies from 0.01 s to 0.11 s. Fig. 7 visualizes
the result.

Supplementary Fig. 7: Gamma band analysis. The average single-channel TMIF in
gamma band for the control and the aphasia group separately, with topoplots at
indicated brain latencies. The spatio-temporal cluster-based permutation test
investigated the difference between the control and aphasia group (control -
aphasia) and identified a cluster (below threshold p<0.05) with the largest
group difference. Brain latencies belonging to the cluster are marked in a
shaded gray area, the channels belonging to the cluster are indicated with a
black dot on the topoplot.* = p<0.05

### Group-specific stability analysis

Supplementary Fig. 8: Stability measures grouped. Within- and between-subjects
stability analysis performed for each group separately. Black dotted line
indicates the average across frequencies. Shaded areas indicate the standard
error of the correlations. The knee point of all panels is indicated with a
vertical dotted line (based on the average across frequencies)

### Demographic information

Supplementary Table 1: Demographics and lesion information of the aphasia
group

ID(n=27) | age | sex |  | Time since
---
stroke
(months)
| Stroke
---
type
| Blood
---
vessel
| Lesioned
---
hemi-
sphere
SLT |  | NBT score
---
(max=276)
| ScreeLing
---
score (max=72)
sub-006 | 86 | m | 22.8 | ischemia | VA | bilateral | yes | 263 | 70
sub-008 | 71 | f | 21.1 | ischemia | MCA | left | no | 273 | 69
sub-009 | 67 | m | 22.6 | ischemia | MCA | left | no | 262 | 65.5
sub-010 | 74 | m | 20.6 | ischemia | PCA | left | no | $\diamond$ | 69
sub-014 | 75 | m | 18.2 | ischemia | MCA | left | yes | 185 | 58
sub-016 | 68 | m | 18.0 | ischemia | MCA | bilateral | yes | 265 | 69
sub-017 | 88 | m | 8.2 | ischemia |  | MCA/
---
PCA
bilateral | no | 245 | 56
sub-018 | 61 | f | 29.4 | ischemia | MCA | left | yes | 263 | 72
sub-019 | 72 | m | 8.6 | ischemia | PCA | left | $\diamond$ | 211 | 51
sub-020 | 42 | m | 18.7 | ischemia | MCA | left | yes | 273 | 71
sub-021 | 81 | m | 8.6 | hemorrhage | $\diamond$ | left | no | 252 | 70
sub-022 | 78 | f | 8.2 | hemorrhage | $\diamond$ | left | yes | 207 | 56
sub-023 | 90 | m | 22.8 | ischemia | LLA | bilateral | no | 265 | 61
sub-024 | 69 | m | 6 | ischemia | MCA | left | yes | 260 | 68.5
sub-025 | 69 | m | 11.5 | ischemia | MCA | left | yes | 197 | 58.5
sub-026 | 71 | m | 31 | ischemia | MCA | left | yes | 250 | 69
sub-027 | 76 | m | 126.2 | ischemia | MCA | left | yes | 254 | 63
sub-028 | 80 | m | 25.2 | ischemia | $\diamond$ | left | no | 195 | 54
sub-029 | 75 | m | 22.6 | ischemia | MCA | left | yes | 193 | 55
sub-030 | 49 | m | 13.1 | hemorrhage | $\diamond$ | left | yes | 217 | 57
sub-031 | 79 | m | 12.9 | ischemia | MCA | left | yes | 263 | 63
sub-032 | 76 | m | 94.8 | ischemia | MCA | left | yes | 3 | 28
sub-034 | 79 | m | 13.4 | $\diamond$ | $\diamond$ | $\diamond$ | yes | 244 | 69
sub-035 | 64 | f | 31.5 | ischemia |  | MCA/
---
ACA
left | yes | 276 | 71
sub-038 | 60 | f | 368.6 | $\diamond$ | $\diamond$ | $\diamond$ | no | 251 | 65
sub-049 | 85 | f | 8.3 | ischemia | PICA | left | yes | 242 | 65.5
sub-050 | 81 | f | 8.7 | ischemia | MCA | left | yes | 134 | 53
Total |  | 73
---
$\pm$ 11
| 20m
---
7f
| 37
---
$\pm$ 71
| 23 ischemia
---
3 hemorrhage
| 17 MCA
---
3 PCA
1 ACA
1 VA
1 LLA
1 PICA
| 22 left
---
4 bilateral
| 21 yes
---
8 no
| 228.58
---
$\pm$ 57.75
| 62.11
---
$\pm$ 9.43

SLT=speech-language therapy; NBT=Dutch naming test (Nederlandse Benoem Test);
VA=vertebral artery; MCA=middle cerebral artery; PCA=posterior cerebral
artery; ACA=anterior cerebral artery; LLA=lateral lenticulostriate arteries;
PICA=posterior inferior cerebellar artery; $\diamond$=data not available

### Correlation matrix frequency bands

Supplementary Table 2: Correlation matrix neural envelope tracking

| IWA | Controls
---|---|---
| broad | delta | theta | alpha | beta | gamma | broad | delta | theta | alpha | beta | gamma
broad | 1 |  |  |  |  |  | 1 |  |  |  |  |
delta | 0.79 | 1 |  |  |  |  | 0.92 | 1 |  |  |  |
theta | 0.15 | 0.01 | 1 |  |  |  | 0.09 | 0.02 | 1 |  |  |
alpha | 0.15 | 0.07 | 0.71 | 1 |  |  | 0.27 | 0.15 | 0.52 | 1 |  |
beta | 0.15 | 0.20 | 0.62 | 0.61 | 1 |  | 0.48 | 0.57 | 0.32 | 0.44 | 1 |
gamma | 0.19 | 0.22 | -0.01 | 0.01 | 0.23 | 1 | 0.12 | 0.18 | -0.18 | -0.22 | 0.26 | 1

Exploratory analysis investigating the collinearity between frequency bands.
The table displays the Pearson correlations for the mean MI (integration
window 0-400 ms).

## References

  * Aerts et al., (2015) Aerts, A., van Mierlo, P., Hartsuiker, R. J., Santens, P., and Letter, M. D. (2015).  Neurophysiological sensitivity for impaired phonological processing in the acute stage of aphasia.  Brain and Language, 149:84–96.
  * Armstrong, (2000) Armstrong, E. (2000).  Aphasic discourse analysis: The story so far.  Aphasiology, 14(9):875–892.
  * Becker and Reinvang, (2007) Becker, F. and Reinvang, I. (2007).  Successful syllable detection in aphasia despite processing impairments as revealed by event-related potentials.  Behavioral and Brain Functions, 3.
  * Brodbeck et al., (2022) Brodbeck, C., Das, P., Gillis, M., Kulasingham, J. P., Bhattasali, S., Gaston, P., Resnik, P., and Simon, J. Z. (2022).  Eelbrain: A python toolkit for time-continuous analysis with temporal response functions.  bioRxiv.
  * Bryant et al., (2019) Bryant, L., Ferguson, A., Valentine, M., and Spencer, E. (2019).  Implementation of discourse analysis in aphasia: investigating the feasibility of a knowledge-to-action intervention.  Aphasiology, 33(1):31–57.
  * Cocquyt et al., (2020) Cocquyt, E. M., Vandewiele, M., Bonnarens, C., Santens, P., and De Letter, M. (2020).  The sensitivity of event-related potentials/fields to logopedic interventions in patients with stroke-related aphasia.  Acta neurologica Belgica, 120(4):805–817.
  * Crosse et al., (2021) Crosse, M. J., Zuk, N. J., Di Liberto, G. M., Nidiffer, A. R., Molholm, S., and Lalor, E. C. (2021).  Linear modeling of neurophysiological responses to speech and other continuous stimuli: Methodological considerations for applied research.  Frontiers in Neuroscience, 15.
  * Dalton et al., (2022) Dalton, S. G., Stark, B. C., Fromm, D., Apple, K., MacWhinney, B., Rensch, A., and Rowedder, M. (2022).  Validation of an automated procedure for calculating core lexicon from transcripts.  Journal of Speech, Language, and Hearing Research, 65(8):2996–3003.
  * De Clercq et al., (2023) De Clercq, P., Vanthornhout, J., Vandermosten, M., and Francart, T. (2023).  Beyond linear neural envelope tracking: a mutual information approach.  Journal of Neural Engineering, 20(2):026007.
  * de Renzi and Ferrai, (1978) de Renzi, E. and Ferrai, C. (1978).  The reporter’s test: A sensitive test to detect expressive disturbances in aphasics.  Cortex, 14(2):279–293.
  * Decruy et al., (2019) Decruy, L., Vanthornhout, J., and Francart, T. (2019).  Evidence for enhanced neural tracking of the speech envelope underlying age-related speech-in-noise difficulties.  Journal of Neurophysiology, 122(2):601–615.
  * Desai et al., (2023) Desai, M., Field, A. M., and Hamilton, L. S. (2023).  Dataset size considerations for robust acoustic and phonetic speech encoding models in eeg.  Frontiers in Human Neuroscience, 16.
  * Devanga et al., (2021) Devanga, S. R., Pollens, R. D., and Glista, S. O. (2021).  Toward developing outcome measures in university-based aphasia programs: Perspectives from the aphasia communication enhancement program.  Perspectives of the ASHA Special Interest Groups, 6(5):1047–1059.
  * Di Liberto and Lalor, (2017) Di Liberto, G. M. and Lalor, E. C. (2017).  Indexing cortical entrainment to natural speech at the phonemic level: Methodological considerations for applied research.  Hearing Research, 348:70–77.
  * Di Liberto et al., (2015) Di Liberto, G. M., O’Sullivan, J. A., and Lalor, E. C. (2015).  Low-frequency cortical entrainment to speech reflects phoneme-level processing.  Current Biology, 25(19):2457–2465.
  * Dial et al., (2021) Dial, H. R., Gnanateja, G. N., Tessmer, R. S., Gorno-Tempini, M. L., Chandrasekaran, B., and Henry, M. L. (2021).  Cortical tracking of the speech envelope in logopenic variant primary progressive aphasia.  Frontiers in Human Neuroscience, 14.
  * Diedenhofen and Musch, (2015) Diedenhofen, B. and Musch, J. (2015).  cocor: a comprehensive solution for the statistical comparison of correlations.  PLoS ONE, 10(3):e0121945.
  * Ding et al., (2016) Ding, N., Melloni, L., Zhang, H., Tian, X., and Poeppel, D. (2016).  Cortical tracking of hierarchical linguistic structures in connected speech.  Nat. Neurosci., 19:158–164.
  * Ding and Simon, (2013) Ding, N. and Simon, J. Z. (2013).  Adaptive temporal encoding leads to a background-insensitive cortical representation of speech.  Journal of Neuroscience, 33(13):5728–5735.
  * El Hachioui et al., (2017) El Hachioui, H., Visch-Brink, E. G., de Lau, L. M., van de Sandt-Koenderman, M. W., Nouwens, F., Koudstaal, P. J., and Dippel, D. W. (2017).  Screening tests for aphasia in patients with stroke: a systematic review.  Journal of Neurology, 264(2):211–220.
  * El Hachioui et al., (2014) El Hachioui, H., Visch-Brink, E. G., Lingsma, H. F., Van De Sandt-Koenderman, M. W., Dippel, D. W., Koudstaal, P. J., and Middelkoop, H. A. (2014).  Nonlinguistic cognitive impairment in poststroke aphasia: A prospective study.  Neurorehabilitation and Neural Repair, 28(3):273–281.
  * Etard and Reichenbach, (2019) Etard, O. and Reichenbach, T. (2019).  Neural speech tracking in the theta and in the delta frequency band differentially encode clarity and comprehension of speech in noise.  Journal of Neuroscience, 39(29):5750–5759.
  * Fonseca et al., (2019) Fonseca, J., Raposo, A., and Martins, I. P. (2019).  Cognitive functioning in chronic post-stroke aphasia.  Applied Neuropsychology: Adult, 26(4):355–364.
  * Francart et al., (2008) Francart, T., Wieringen, A. V., and Wouters, J. (2008).  Apex 3: a multi-purpose test platform for auditory psychophysical experiments.  J Neurosci Methods, 172:283–293.
  * Fujioka et al., (2015) Fujioka, T., Ross, B., and Trainor, L. J. (2015).  Beta-band oscillations represent auditory beat and its metrical hierarchy in perception and imagery.  Journal of Neuroscience, 35(45):15187–15198.
  * Gillis et al., (2022) Gillis, M., Van Canneyt, J., Francart, T., and Vanthornhout, J. (2022).  Neural tracking as a diagnostic tool to assess the auditory pathway.  Hearing Research, page 108607.
  * Gillis et al., (2021) Gillis, M., Vanthornhout, J., Simon, J. Z., Francart, T., and Brodbeck, C. (2021).  Neural markers of speech comprehension: Measuring eeg tracking of linguistic speech representations, controlling the speech acoustics.  The Journal of Neuroscience, 41(50):10316–10329.
  * Gillis, Kries et al., (2023) Gillis, Kries, Vandermosten, M., and Francart, T. (2023).  Neural tracking of linguistic and acoustic speech representations decreases with advancing age.  NeuroImage, 267(119841):1–16.
  * Giraud and Poeppel, (2012) Giraud, A. and Poeppel, D. (2012).  Cortical oscillations and speech processing: emerging computational principles and operations.  Nat. Neurosci., 15:511–517.
  * Gross et al., (2013) Gross, J., Hoogenboom, N., Thut, G., Schyns, P., Panzeri, S., Belin, P., and Garrod, S. (2013).  Speech rhythms and multiplexed oscillatory sensory coding in the human brain.  PLoS Biology, 11(12):e1001752.
  * Hamilton and Huth, (2018) Hamilton, L. S. and Huth, A. G. (2018).  The revolution will not be controlled: natural stimuli in speech neuroscience.  Language, cognition and neuroscience, 35(5):573–582.
  * Hyafil et al., (2015) Hyafil, A., Giraud, A.-L., Fontolan, L., and Gutkin, B. (2015).  Neural cross-frequency coupling: Connecting architectures, mechanisms, and functions.  Trends in Neurosciences, 38(11):725–740.
  * Ilvonen et al., (2001) Ilvonen, T. M., Kujala, T., Tervaniemi, M., Salonen, O., Näätänen, R., and Pekkonen, E. (2001).  The processing of sound duration after left hemisphere stroke: Event-related potential and behavioral evidence.  Psychophysiology, 38:622–628.
  * Ince et al., (2017) Ince, R. A. A., Giordano, B., Kayser, C., Rousselet, G., Gross, J., and Schyns, P. (2017).  A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula.  Human brain mapping, 38(3):1541–1573.
  * Jamal et al., (2017) Jamal, N., Shanta, S., Mahmud, F., and Sha’abani, M. (2017).  Automatic speech recognition (asr) based approach for speech therapy of aphasic patients: A review.  AIP Conference Proceedings, 1883(1):020028.
  * Jansen et al., (2012) Jansen, S., Luts, H., Wagener, K. C., Kollmeier, B., Del Rio, M., Dauman, R., James, C., Fraysse, B., Vormès, E., Frachet, B., Wouters, J., and van Wieringen, A. (2012).  Comparison of three types of french speech-in-noise tests: A multi-center study.  International Journal of Audiology, 51(3):164–173.
  * Johnson et al., (2019) Johnson, L., Basilakos, A., Yourganov, G., Cai, B., Bonilha, L., Rorden, C., and Fridriksson, J. (2019).  Progression of aphasia severity in the chronic stages of stroke.  American Journal of Speech-Language Pathology, 28(2):639–649.
  * Kandylaki and Bornkessel-Schlesewsky, (2019) Kandylaki, K. D. and Bornkessel-Schlesewsky, I. (2019).  From story comprehension to the neurobiology of language.  Language, Cognition and Neuroscience, 34(4):405–410.
  * Kaufeld et al., (2020) Kaufeld, G., Bosker, H. R., Alday, P. M., Meyer, A. S., and Martin, A. E. (2020).  Linguistic structure and meaning organize neural oscillations into a content-specific hierarchy.  Journal of Neuroscience, 40(49):9467–9475.
  * Keitel et al., (2018) Keitel, A., Gross, J., and Kayser, C. (2018).  Perceptually relevant speech tracking in auditory and motor cortex reflects distinct linguistic features.  PLoS Biology, 16(3):e2004473.
  * Kertesz, (1982) Kertesz, A. (1982).  Western aphasia battery.  New York: Grune and Stratton.
  * Kim et al., (2022) Kim, H., Berube, S., and Hillis, A. E. (2022).  Core lexicon in aphasia: A longitudinal study.  Aphasiology, 0(0):1–13.
  * Kries et al., (2022) Kries, J., De Clercq, P., Lemmens, R., Francart, T., and Vandermosten, M. (2022).  Tuning in on auditory details is difficult: Individuals with aphasia show impaired acoustic and phonemic processing.  bioRxiv.
  * Kulasingham et al., (2020) Kulasingham, J. P., Brodbeck, C., Presacco, A., Kuchinsky, S. E., Anderson, S., and Simon, J. Z. (2020).  High gamma cortical processing of continuous speech in younger and older listeners.  NeuroImage, 222:117291.
  * Lalor et al., (2009) Lalor, E. C., Power, A. J., Reilly, R. B., and Foxe, J. J. (2009).  Resolving precise temporal processing properties of the auditory system using continuous stimuli.  Journal of Neurophysiology, 102(1):349–359.
  * Le et al., (2018) Le, D., Licata, K., and Mower Provost, E. (2018).  Automatic quantitative analysis of spontaneous aphasic speech.  Speech Communication, 100:1–12.
  * Lesenfants et al., (2019) Lesenfants, D., Vanthornhout, J., Verschueren, E., Decruy, L., and Francart, T. (2019).  Predicting individual speech intelligibility from the neural tracking of acoustic- and phonetic-level speech representations.  Hearing Research, 380:1–9.
  * Lesser and Algar, (1995) Lesser, R. and Algar, L. (1995).  Towards combining the cognitive neuropsychological and the pragmatic in aphasia therapy.  Neuropsychological Rehabilitation, 5(1-2):67–92.
  * Lizarazu et al., (2021) Lizarazu, M., Lallier, M., Bourguignon, M., Carreiras, M., and Molinaro, N. (2021).  Impaired neural response to speech edges in dyslexia.  Cortex, 135:207–218.
  * Lizarazu et al., (2019) Lizarazu, M., Lallier, M., and Molinaro, N. (2019).  Phase amplitude coupling between theta and gamma oscillations adapts to speech rate.  Annals of the New York Academy of Sciences, 1453(1):140–152.
  * Mandke et al., (2022) Mandke, K., Flanagan, S., Macfarlane, A., Gabrielczyk, F., Wilson, A., Gross, J., and Goswami, U. (2022).  Neural sampling of the speech signal at different timescales by children with dyslexia.  NeuroImage, 253:119077.
  * Maris and Oostenveld, (2007) Maris, E. and Oostenveld, R. (2007).  Nonparametric statistical testing of eeg- and meg-data.  Journal of Neuroscience Methods, 164(1):177–190.
  * Mesik and Wojtczak, (2022) Mesik, J. and Wojtczak, M. (2022).  The effects of data quantity on performance of temporal response function analyses of natural speech processing.  bioRxiv.
  * Mullen et al., (2015) Mullen, T. R., Kothe, C. A. E., Chi, Y. M., Ojeda, A., Kerth, T., Makeig, S., Jung, T. P., and Cauwenberghs, G. (2015).  Real-time neuroimaging and cognitive monitoring using wearable dry eeg.  IEEE Transactions on Biomedical Engineering, 11(62):2553–2567.
  * Ofek et al., (2013) Ofek, E., Purdy, S. C., Ali, G., Webster, T., Gharahdaghi, N., and McCann, C. M. (2013).  Processing of emotional words after stroke: An electrophysiological study.  Clinical Neurophysiology, 124:1771–1778.
  * Papathanasiou and Coppens, (2017) Papathanasiou, I. and Coppens, P. (2017).  Aphasia and related neurogenic communication disorders.  Burlington, MA: Jones and Bartlett Learning.
  * Pedregosa et al., (2011) Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011).  Scikit-learn: Machine learning in python.  J. Mach. Learn. Res., 12:2825–2830.
  * Pedroni et al., (2019) Pedroni, A., Bahreini, A., and Langer, N. (2019).  Automagic: Standardized preprocessing of big eeg data.  NeuroImage, 200(null):460–473.
  * Peelle and Davis, (2012) Peelle, J. E. and Davis, M. H. (2012).  Neural oscillations carry speech rhythm through to comprehension.  Front Psychol, 3.
  * Perez et al., (2022) Perez, A., Davis, M. H., Ince, R. A. A., Zhang, H., Zhanao, F., Lamarca, M., Lambon Ralph, M. A., and Monahan, P. J. (2022).  Timing of brain entrainment to the speech envelope during speaking, listening and self-listening.  Cognition, 224:105051.
  * Pion-Tonachini et al., (2019) Pion-Tonachini, L., Kreutz-Delgado, K., and Makeig, S. (2019).  Iclabel: An automated electroencephalographic independent component classifier, dataset, and website.  NeuroImage, 198(null):181–197.
  * Pulvermüller et al., (2004) Pulvermüller, F., Mohr, B., and Lutzenberger, W. (2004).  Neurophysiological correlates of word and pseudo-word processing in well-recovered aphasics and patients with right hemispheric stroke.  Psychophysiology, 41:584–591.
  * Robson et al., (2017) Robson, H., Pilkington, E., Evans, L., DeLuca, V., and Keidel, J. L. (2017).  Phonological and semantic processing during comprehension in Wernicke’s aphasia: An N400 and Phonological Mapping Negativity Study.  Neuropsychologia, 100(October 2016):144–154.
  * Rohde et al., (2018) Rohde, A., Worrall, L., Godecke, E., O’Halloran, R., Farrell, A., and Massey, M. (2018).  Diagnosis of aphasia in stroke populations: A systematic review of language tests.  PLoS ONE, 13(3):e0194143.
  * Satopaa et al., (2011) Satopaa, V., Albrecht, J., Irwin, D., and Raghavan, B. (2011).  Finding a "kneedle" in a haystack: Detecting knee points in system behavior.  2011 31st International Conference on Distributed Computing Systems Workshops, pages 166–171.
  * Shannon et al., (1995) Shannon, R. V., Zeng, F. G., V, K., Wygonski, J., and S, E. M. (1995).  Speech recognition with primarily temporal cues.  Science, 270(5234):303–304.
  * Stark et al., (2021) Stark, B. C., Dutta, M., Murray, L. L., Fromm, D., Bryant, L., Harmon, T. G., Ramage, A. E., and Roberts, A. C. (2021).  Spoken discourse assessment and analysis in aphasia: An international survey of current practices.  Journal of Speech, Language, and Hearing Research, 64(11):4366–4389.
  * Søndergaard et al., (2012) Søndergaard, P., Torrésani, B., and Balazs, P. (2012).  The linear time frequency analysis toolbox.  International Journal of Wavelets Multiresolution and Information Processing, 10:1250032.
  * Van Ewijk et al., (2020) Van Ewijk, E., Dijkhuis, L., Hofs-Van Kats, M., Hendrickx-Jessurun, M., Wijngaarden, M., and De Hilster, C. (2020).  Nederlandse Benoem Test.  Bohn stafleu van loghum.
  * Vanthornhout et al., (2018) Vanthornhout, J., Decruy, L., Wouters, J., Simon, J. Z., and Francart, T. (2018).  Speech intelligibility predicted from neural entrainment of the speech envelope.  JARO - Journal of the Association for Research in Otolaryngology, 19(2):181–191.
  * Verschueren et al., (2022) Verschueren, E., Gillis, M., Decruy, L., Vanthornhout, J., and Francart, T. (2022).  Speech understanding oppositely affects acoustic and linguistic neural tracking in a speech rate manipulation paradigm.  Journal of Neuroscience, 42(39):7442–7453.
  * Visch-Brink et al., (2010) Visch-Brink, E., Van de Sandt-Koenderman, M., and El Hachioui, H. (2010).  ScreeLing.  Houten: Bohn Stafleu Van Loghum.
  * Wallace and Kimelman, (2013) Wallace, S. E. and Kimelman, M. D. (2013).  Generalization of word retrieval following semantic feature treatment.  Neurorehabilitation, 32(4):899–913.
  * Wöstmann et al., (2017) Wöstmann, M., Lim, S., and Obleser, J. (2017).  The human neural alpha response to speech is a proxy of attentional control.  Cerebral cortex, 27(6):3307–3317.
  * Xu et al., (2022) Xu, N., Zhao, B., Luo, L., Zhang, K., Shao, X., Luan, G., Wang, Q., Hu, W., and Wang, Q. (2022).  Two stages of speech envelope tracking in human auditory cortex modulated by speech intelligibility.  Cerebral Cortex, 33(5):2215–2228.
  * Zan et al., (2020) Zan, P., Presacco, A., Anderson, S., and Simon, J. Z. (2020).  Exaggerated cortical representation of speech in older listeners: mutual information analysis.  Journal of Neurophysiology, 124(4):1152–1164.
  * Zou et al., (2021) Zou, J., Xu, C., Luo, C., Jin, P., Gao, J., Li, J., Gao, J., Ding, N., and Luo, B. (2021).  θ-band cortical tracking of the speech envelope shows the linear phase property.  eNeuro, 8(4).