Evaluating potential EEG-indicators for auditory attention to speech inrealistic environmental noise
Ehsan Eqlimi(1) , Annelies Bockstael(2) , Bert De Coensel(1) and Dick Botteldooren(1)(1) WAVES research group, Department of Information Technology (INTEC), Ghent University (UGent), Belgium(2) École d’orthophonie et d’audiologie, Université de Montréal, Québec, H3N 1X7, Canada

Abstract
The human brain is remarkably capable of perceiving relevant sounds in noisy environments but the underlying interplay of neurophysiology and acoustics is still being investigated. Cortical processing of these sounds in the brain depends on attentional demand. One of the most important issues is how to identify whether a person is paying attention
to the relevant sounds or not. The aim of this study was to explore the potential of single-trial electroencephalography(EEG) indicators to distinguish the cortical representation of three sequential tasks — attentive listening to lecturesin background noise, attentive and inattentive listening to background noise alone. Three types of environmental
noise, including multi-talker babble, fluctuating traffic and highway sounds were employed as the background during
the first task and the stimulus during the second and third tasks. 23 healthy volunteers were exposed to these threetasks while 64-channels EEG signals were recorded. Alpha-band spectral characteristics (peak frequency and power)
were investigated as potential indicators of attention and cortical inhibition. Furthermore, based on the hypothesisof self-similarity as excitation-inhibition balance, long-range temporal correlation of alpha-band activity was quantified based on detrended fluctuation analysis. Finally, the hypothesis of speech envelope entrainment of brain activity
motivated to estimate the delta absolute power for investigating the attended sound. Considering the participant asa random factor, a linear mixed-effect regression was employed to model the estimated indicators as a function oflistening task, EEG channel cluster, and background noise. Strong significant differences were found that support our
hypotheses that auditory attention to speech can be observed via EEG-indicators.
Keywords: auditory attention, EEG, cortical inhibition, alpha activity, speech in noise

1

INTRODUCTION

Humans in everyday life are often flooded with a cacophony of target sounds and irrelevant information
originating from ecologically valid contexts. Auditory attention plays a key role in sound perception by directing both cognitive and sensory resources to the target sounds. Environmental noise (e.g., multi-talker babble,
fluctuating traffic and highway sounds) impairs speech intelligibility and may distract attention away from thenarrative. In this regard, one of the most important issues is how to identify whether a person is paying attention to the presented verbal information or not. Multichannel electroencephalography (EEG) at the scalp asa cheap and portable tool with high temporal resolution provides the brain electrical activity, which could be
adopted for identifying neural correlates of the auditory attention.
In cognitive sciences, attention is referred to as the mechanism that controls information processing andallocates processing resources to target stimuli [1]. Since attention is not a single and unidirectional process,it could be modulated by externally (bottom-up) driven factors (e.g., sudden attention to a phone ringing) orinternally (top-down) factors (e.g., a student focusing on a teacher’s lecture in presence of background noise) [2].
Two cognitive control functions are exerted due to attention: (1) excitation and maintenance of task-relevant
processes and (2) inhibition of task-irrelevant processes [3].
The alpha-as-inhibition suggests that alpha-band activity (approx. 8 − 13 Hz), beyond neural idling state,
could reflect a top-down mechanism that inhibits task-irrelevant information [4, 5]. It has been proposed that
increased task-related alpha power could be linked to inhibitory control [6]. Indeed, synchronized alpha reflects
neural inhibition while desynchronized alpha indicates release from the inhibitory control. Moreover, it has beenargued that task-relevant regions exhibit decreased alpha power while task-irrelevant regions exhibit increasedalpha power [7]. There are several studies suggesting that the resting-state alpha power could predict the effi-7631

ciency of cognitive task performance. For example, increased resting-state alpha power in the upper alpha band
could be a clue for the good performance of the actual task [8].
In addition to alpha power, several studies have been argued about the relationship between task demand
and alpha peak or center of gravity frequency (also known as individual alpha frequency). For instance, it has
been demonstrated that alpha peak frequency is linked to the input level (equivalently the number of spiking
neurons) such that if the input level increases respect to baseline level, the alpha peak frequency accelerates until
the oscillation becomes unstable, being then replaced by slow alpha peak frequency [9, 10, 11]. On the other
hand, there are several studies suggesting that with increasing attentional demand and cognitive load, the alphafrequency decreases [1, 12, 13, 14]. This decreased alpha frequency could likely be linked to the unstable stateand overloaded attention capacity. Furthermore, it has been suggested [10] that increased alpha peak frequency
could be accompanied by a decrease in alpha peak power, which may imply that task-relevant regions could
exhibit the accelerated alpha peak frequency during task performance compared to task-irrelevant regions.
Furthermore, it has been proposed that ongoing neural oscillations hold a memory of their own dynamics
on fairly long time scales based on long-range temporal correlation (LRTC) idea [15, 16, 17]. This behavior
could reflect a critical state due to an optimal balance between excitation (maintaining relevant information)and inhibition (suppressing irrelevant information) states during cognitive tasks. The relationship between LRTCand cognitive performance such as attention, working memory and decision making has been investigated. For
instance, [18] showed that decreased alpha-band LRTC compared to the resting-state correlates with better attentional performance.
On the other hand, it has been suggested that low-frequency speech envelope entrainment of brain activity
could be robust against different background noise [19]. Moreover, it has been proposed that attention could
modulate low frequency (approx. < 8 Hz) cortical representation of speech based on the envelope following [20].
This effect is termed the attentional gain of cortical speech representation. Although the coupling between delta
activity (approx. 1 − 4 Hz) and speech envelope usually is employed to capture the speech-brain entrainment, inthe present study, we have focused on the delta absolute power as a measure of speech envelope following andattentional gain.
The above discussions inspired us to investigate the mentioned hypotheses in a scenario with three listening
tasks. For this purpose, twenty-three participants were exposed to three sequential listening conditions while64-channels EEG signals were recorded. Firstly, all participants were instructed to pay attention to lectures inbackground noise and then to background noise alone. The background sounds were also presented separately
during a resting state where participants were asked not to pay attention to sound. EEG-indicators (alpha peakfrequency, alpha peak power, alpha-band long-range temporal correlation, and delta absolute power) were separately modeled as a function of listening task, type of noise and EEG channel cluster to discover the statisticallysignificant differences using linear mixed-effect regression with participant included as a random factor.

2

MATERIALS AND METHODS

2.1 Participants
Twenty-three normal hearing and English speaking volunteers (young adults and approximately same age)with no history of neurological complications participated in this experiment. All participants signed the informed consent and the protocol was approved by the ethical committee.
2.2 Experimental protocol
All participant were exposed to three sequential tasks: (1) lecture attended (LA): focusing attention to theinformative lecture in the presence of background noise, mainly an outward-oriented process, (2) backgroundattended (BA): being attentive to the background sound with embedded salient events, which is a sensory-driven
process, and (3) background unattended (BUA): not paying attention to any sound. The experimental protocol
is schematized in Figure 1.
The order of presentation in lecture attended condition was completely random in both lecture and background noise while assuring the two lectures in silence were not presented in succession. Furthermore, eachlecture was presented only once so that each participant was exposed to thirteen different lectures. Each participant completed a written exam on presented lectures and salient events in LA and BA tasks, respectively. They
were instructed to keep in mind the information from the lectures during LA task and the number of individual
audible salient events (phone ringing, car horn’s honk, and emergency siren) during BA task.

7632

Randomized order for each participant

Fixed order for each participant

Fixed order for each participant

multitalker

multitalker

L.1 + pink noise
L.2 + multitalker
L.3 + fluctuating traffic

L.4 + highway
L.5 + fluctuating traffic(1) Paying
attention tolectures (L)LA

L.6 + multitalker
L.7 + pink noise(2) Paying
attention toBA
L.8 + highway

L.9 + pink noise
L.10 +fluctuating
L.11 + multitalker

highway

highway

fluctuating traffic

fluctuating traffic

highway

highway

fluctuating traffic

fluctuating traffic

multitalker
fluctuating traffic(3) Completingan exam onpresented
information(4) Not paying
attention to

BUA

multitalker

multitalker

fluctuating traffic
multitalker

highway

highway

multitalker

multitalker

highway

highway

fluctuating traffic

fluctuating traffic

L.12 + highway

L.13 + pink noise

Figure 1. Schematic protocol of attentive & inattentive listening
2.3 Stimuli
During the LA condition, a mixture of target lectures and non-target background noises were presented.
Thirteen English lectures were chosen about obscure topics to limit a priori knowledge. The lectures had aduration of about five minutes and were presented at a level of about 68 dB(A). The lectures were read by anative male English speaker with teaching skills and recorded in an anechoic room.
Background noise fragments included three types of environmental noise: (1) HW: highway sound withmatching salient sounds of emergency vehicles (2) FT: fluctuating city street sound dominated by individual
passage of cars, some birds singing and pass-by of honking car as salient sounds (3) MT: multi-talker babble
with matching salient sounds of ring tone of a telephone. The background noise and lecture signals have been
mixed with equivalent levels of 63 dB(A) and 68 dB(A), respectively. This assures that the noise does not mask
the lecture energetically. The FT noise during LA had as expected more instances of bad SNR (below 0 dB)
compared to HW noise, but also more instances of optimal SNR (more than 5 dB). The MT noise is generally
more hampering than the HW noise, but compared to the FT noise had fewer instances of bad SNR. As shownin Figure 1, during LA condition, in addition to the mixture of lecture and background noise, four lectures ofduration five minutes with low pink (PK) noise (i.e. power spectral density is proportional to 1f ) at a level of
35 dB(A) were presented. During BA and BUA conditions, each background noise fragment had a duration of
about three minutes and was presented at a level of 63 dB(A).
2.4 EEG recordings and preprocessing
Continuous EEG signals were recorded with a BioSemi system from 64 scalp locations (10-20 standard
layout). Mono playback was done during the EEG recordings with one loudspeaker at one meter from theparticipant. The data were recorded with Fpz reference and at a sampling frequency of 2048 Hz. Off-line
preprocessing was carried out in EEGLAB [21] package. The data re-referenced to nose electrode and downsampled to 512 Hz. A finite impulse response (FIR) and hamming windowed-sinc bandpass filter of order 3380
between 0.5 − 134 Hz was applied to remove extremely slow drifts and sharp oscillations.
EEG signals were cleaned up in two steps. At first, non-repeating big artifacts were removed based on
visual inspection. In the second step, infomax independent components analysis (ICA) with EEGLAB [21]default settings was applied to identify and remove eye blink and movement artifacts.
Since playing audio file typically has a latency of a few milliseconds, the sound was recorded together
with the EEG on a free channel and this channel was used to were needed to synchronize with the presented
audio signal. For this purpose, at first, the presented audio files were re-sampled to 512 Hz and then the
cross-correlation between re-sampled audio signals and recorded sound signals together with the EEG was calculated. The lag corresponding to maximum cross-correlation is the delay in audio files with respect to EEG
measurement. To compensate for this delay, all 64-channels EEG signals were shifted with estimated delays.

7633

2.5 EEG signal processing
The EEG-indicators for auditory attention were calculated for each participant per EEG channel and listening
task. Firstly, alpha peak frequency and alpha peak power (APF and APP, respectively) were estimated basedon multiple signal classification algorithm in order to investigate alpha-as-inhibition. APF is defined as thefrequency of alpha-band oscillations which demonstrates maximal power. Second, in order to capture the dynamic properties of EEG signals and estimate the power-law scaling exponent, alpha-band long-range temporalcorrelation (LRTC) was quantified based on detrended fluctuation analysis. Finally, delta absolute power (DAP,
approx. 1 − 4 Hz) as a measure of speech envelope following of brain activity was estimated using Welch’s
power spectral density (PSD) method (a Hamming window of length 512 with 256 samples overlap). The next
subsections briefly explain our approaches to estimate APF, APP, and LRTC.
2.5.1 Alpha peak frequency and power based on multiple signal classification
APF and APP could be quantified based on PSD of EEG signals. There are several techniques to estimate
PSD such as fast Fourier transform (FFT)-based and subspace-based methods. Subspace-based methods (e.g.,
root-multiple signal classification (root-MUSIC) [22]) provide higher frequency resolution compared to FFTbased methods (e.g., periodogram method).
Root-MUSIC as a subspace-based method estimates the frequency content of a signal using an eigenspacemethod. This method assumes that a signal consists of P complex exponentials in the presence of Gaussian
white noise. Given a real-valued observation vector, x ∈ R1×N (N is the number of signal samples), a rectangular
Toeplitz matrix, Rx ∈ R(N−2P+1)×2P could be estimated such that generates an autocorrelation estimate for x
based on a (2P − 1)th-order prediction error model. If eigenvalues of RTx Rx ∈ R2P×2P are sorted in decreasingorder, the eigenvectors corresponding to the P largest eigenvalues (i.e. directions of largest variability) span
the signal subspace. The remaining P eigenvectors span the orthogonal space, where there is only noise. A
polynomial, d, could be formed consisting of a sum of polynomials given by the product of the noise subspace
eigenvectors and the reversed and conjugated version. Finally, the frequency estimates, f, could be determinedas the angular positions of P roots of d which are located nearest the unit circle [23]. In this paper, thepreprocessed EEG signals were first bandpass filtered at 8 − 13 Hz. Second, above mentioned root-MUSIC
algorithm was performed on each filtered EEG channel with P = 2 as the dimension of the signal subspace.
Third, the maximum power (APP in µV2 ) and corresponding frequency (APF in Hz) were found.
2.5.2 Long-range temporal correlation based on detrended fluctuation analysis
The processes that do not have a characteristic scale (i.e., scale-free processes) cannot be described completely in terms of the average concept (e.g., peak frequency). There is sufficient evidence that EEG timeseries exhibit the scale-free dynamics [24]. One of the successful methods to analyze these scale-free signals is
long-range temporal correlation (LRTC). LRTC has been developed to quantify how much future dynamics of asignal are influenced by past temporal events [16].
In fractal geometry, LRTC could be interpreted by a self-similarity behavior, which suggests the signal dynamics are similar in different time scales. One of the most common techniques to quantify LRTC is detrended
fluctuation analysis (DFA) [25]. The presence of a trend in the signal can cause an overestimation for LRTC.
DFA tries to eliminate the trend effect on LRTC. Indeed, DFA is employed to quantify how slowly the autocorrelations of signals decay in power-law behavior (i.e., a, scaling exponent). If 0.5 < a < 1, the signal likely
exhibits strong LRTC and obeys the power-law behavior.
We employed the DFA algorithm which has been suggested in [26] to quantify LRTC for each EEG channel.
First, we defined a set of window size, s, which are equally spaced on a logarithmic scale between 2.5 − 180
seconds (180 seconds is the duration of signals during BA and BUA). The amplitude envelope of the bandpass
filtered signal (8 − 13 Hz), e, based on Hilbert transform was estimated. Then, the cumulative sum of theamplitude envelope, c, was split into a set of n separated time series (Wt ) of length t ∈ s, which have 50%
overlap. For each window, wti ∈ Wt , the linear trend was removed using a least squares method and obtained
the detrended version, ŵti . After calculating the standard deviation of ŵti i.e., σŵti , fluctuation function as themean standard deviation of all windows was computed, f̄(t) = n1 ∑ni=1 σŵti . Finally, we plotted the fluctuation
function, f̄(t), along t on logarithmic axes. We computed the slope of the trend line (scaling exponent) in therange of 5 − 18 seconds (such that filter effect is negligible [26]) using the linear regression as a measure for
LRTC.

7634

3

Results

APF, log(APP), log(DAP) and alpha LRTC were modeled based on a linear mixed regression as a function oflistening task (LA, BUA, BA), channel cluster (occipital, parietal, central, frontal, left and right temporal), andtype of noise (highway (HW), fluctuating traffic (FT), multi-talker babble (MT) and pink (PK)). The participant
was included as a random factor. Tables 1 and 2 report the significance relationships for inter-noise comparisonof task effect and inter-task comparison of background noise effect on EEG-indicators, respectively. Effect ofchannel cluster on APF is described in subsection 3.3. In each 4 × 4 and 3 × 3 sub-tables, upper triangularelements denote p-values, lower triangular elements denote which noise or task results in higher values in termsof given indicator and main diagonal elements denote which noise or task results in the maximum/minimum
values in terms of the given indicator. For example, in Table 1, MT log(DAP) is significantly higher than thatof HW during LA because (1, 2) and (2, 1) elements of matrix corresponding to LA and log(DAP) are < 0.001
and MT, respectively. Note (i, j) indicates the indices of the row and column of the matrix, respectively.
3.1 Inter-noise comparison of task effect on EEG-indicators
The results of statistical analysis for inter-noise comparison of task effect on EEG-indicators were reported
in Table 1. During LA in silence (PK), log(DAP) is significantly higher than that of HW and MT. This finding
might be due to the strong speech envelope following where there is no background noise. However, there
is no significant difference in log(DAP) between PK and FT during LA. On the other hand, log(DAP) during
LA is significantly the lowest in HW i.e., the lowest envelope following. During BA task, log(DAP) in FT isslight significantly higher than that of MT. There are no significant differences in terms of log(DAP) for othercomparisons during BA. However, during BUA, log(DAP) in MT and FT are significantly the lowest and thehighest, respectively. During BUA, log(DAP) difference between FT and HW is slightly significant. Since task
demand during BUA is inattentive listening, decreased envelope following are expected especially in MT.
APF during LA task in HW is significantly the highest compared to other types of noise. Since increased
attentional demand in an unstable state could tend to reduce APF (as we discussed in Section 1), the drop in
attentional performance during LA in HW is the lowest compared to other types of noise. It is worth to notethat the drop in attentional performance could be interpreted by the overloaded attentional capacity. During BA
task in MT, APF is significantly the lowest due to increased task demand or attentional drop. This finding
is consistent with the fact that the multi-talker babble is more hampering and could be more difficult to pay
attention to it. Contrary, APF during BUA in MT is significantly the highest, which implies attracting attention
when participants try not to attend the multi-talker babble sounds. In fact, during inattentive listening to MT,
the attentional demand increases in a stable state and tends to accelerated APF.
Log(APP) during LA in HW is significantly the highest, which reflects the easiest inhibition compared toother noises. Moreover, log(APP) during BUA in MT and HW is significantly the lowest and the highest, implying the most difficult and the easiest inhibition compared to other noises, respectively. These findings support
the hypothesis [1] that small upper alpha event-related desynchronization (ERD) values (equivalently increased
APP), which may be accompanied by large lower alpha ERD, could reflect easier task performance. During the
BA task, there are no significant differences in terms of log(APP) likely due to no need for inhibition.
LRTC of alpha activity during LA task in MT is significantly the highest. Since LRTC could capture selfsimilarity behavior as excitation-inhibition balance, increased LRTC leads to increased excitation-inhibition balance. As a result, both excitation and inhibition are required to pay attention to lecture in MT. In agreementwith previous researches [18], during the attentional task, unlike the resting state, LRTC and attentional performance could have an inverse relationship. During LA in silence (PK), it is expected that excitation is performed
more than the inhibition and tend to decreased LRTC. LRTC of alpha activity during BA task in FT is significantly the lowest due to more activity in excitation mode (less excitation-inhibition balance). In the case of
BUA (could be equivalent with the resting state) in MT, LRTC is significantly the highest because participants
try to inhibit the sound but their attentions are drawn to verbal information (more balance between excitationand inhibition and LRTC).
3.2 Inter-task comparison of background noise effect on EEG-indicators
The results of statistical analysis for inter-task comparison of background noise effect on EEG-indicators
were reported in Table 2. Log(DAP) is significantly the highest during MT and FT in LA compared to othertasks due to more speech envelope following. Moreover, log(DAP) during HW in LA is slight significantly
higher than that of HW in BUA.
As we discussed in Section 1, decreased APF could be an indicator of the attentional demand in an unstable

7635

state due to overloaded attentional capacity. According to Table 2, APF is significantly the lowest during MT
in BA compared to other tasks, which implies MT in BA is likely the most difficult condition to perform thetask. On the contrary, MT in BUA exhibits the maximum APF compared to other tasks. However, APF during
FT noise exhibits an opposite result i.e., it is the maximum for BA and the minimum for BUA likely due tothe more attentional drop during BUA compared to LA and BA (i.e., a stable state).
On the other hand, log(APP) is significantly the lowest during MT in BA, implying the most difficult task
compared to other tasks. In addition, log(APP) is significantly the highest during HW and FT in BUA, which
implies the easier inhibition compared to LA. This finding is in line with [8] where has been suggested that thedecreased alpha power during task performance (likely equivalent to LA and BA) could be predicted by highlevels of resting-state alpha activity (likely equivalent to BUA).
Alpha LRTC may increase when both excitation and inhibition are evoked. Based on this hypothesis, alpha
LRTC is significantly the lowest during MT in BA (less need for inhibition). However, alpha LRTC during MT
in BUA is significantly higher than MT in LA. This finding is in agreement with [18], where decreased LRTC
has been observed during attentional demand (LA) compared to rest (BUA). The same finding was obtained in
FT but alpha LRTC during FT in BA is slight significantly lower than that of LA. For HW case, alpha LRTC
values in LA and BUA are significantly the lowest and the highest compared to other tasks, respectively.
3.3 Effect of channel cluster on APF
In connection with the inter-task comparison of channel cluster effect on APF, during LA and BUA tasks
due to effort for inhibition, APF in right and left temporal clusters are significantly higher than occipital cluster(p < 10−5 and p < 0.001, respectively). This finding is in agreement with the hypothesis that APF (unlike
APP) increases in the task-related region (i.e., auditory cortex) compared to the occipital region. In the opposite
direction, during the BA task likely due to less need for inhibition, occipital APF is significantly higher thanthat of temporal clusters (p < 0.001). On the other hand, regarding the inter-noise comparison of channel
cluster effect on APF, during LA task with pink noise (LA in silence), there is no significant difference between
occipital and right/left temporal clusters (likely due to less need of inhibition). Contrariwise, during multi-talker,
highway and fluctuating traffic due to effort for inhibition, APF in right/left temporal clusters is significantly
higher than the occipital region (p < 0.001). During BA with three types of noise, occipital APF is significantly
higher than that of temporal clusters (p < 10−4 ) likely due to less need for inhibition. During BUA task withmulti-talker noise, APF in the right temporal cluster is significantly higher than the occipital APF (p < 0.001).
However, there is no significant difference between temporal and occipital clusters during BUA in highway andfluctuating traffic.
Table 1. Significance relationships for inter-noise comparison oftask effect on EEG-indicators based on a linear mixed-effect model
LA
MT
MT

HW

- < 0.001

PK

MT

HW

-

0.0381--< 0.001 < 0.001

-

-

BUA
FT

MT

HW

FT

0.0872 Min 0.0088 < 0.001

Log(DAP) HW MT

Min

-

HW

-

0.0777

-

FT

-

-

FT

-

-

FT

FT

Max

PK PK

PK

-

-

-

-

-

-

-

-

Min < 10−5 < 10−5 Max < 10−4 < 10−4

FT

MT
APF

-

HW HW

< 10−4--Max< 10−4< 10−4

HW

-

-

MT

-

-

FT

-

HW

-

-

FT

-

-

MT

-

-

PK

-

HW

-

-

-

-

-

-

-

-

-

-

-

-

-

Min < 0.001 < 0.001

MT HW < 10−4
Log(APP) HW-Max< 10−4< 10−4

-

-

-

HW

Max

0.0084

FT

-

HW

-

-

-

-

-

FT

HW

-

PK

-

HW

-

-

-

-

-

-

-

-

MT Max < 10−6 < 10−6

7636

BA

FT

< 10−6-< 10−4 Max < 10−4 < 10−4

Alpha LRTC HW MT--< 10−6--< 10−4 MT

-

-

FT MT

-

-

1.3 × 10−6 MT

HW

Min

MT

-

-

PK MT

HW

FT

-

-

-

-

-

Min

-

Table 2. Significance relationships for inter-task comparison of background noise
effect on EEG-indicators based on a linear mixed-effect model
MT
LA

BA

BUA

LA

BA

< 10−6---

3.27 × 10−6

-

-

BA

Min

LA Max < 10−6
Log(DAP)BALABUA LA

APF

-

BA

LA

BUA BUA

FT
BUA

LA

BA

BUA

0.0295 Max < 10−5 < 10−5
-

LA

-

-

-

-

LA

-

-

LA

< 10−10 < 10−10--< 10−4-0.00165 < 0.001

Min< 10−10--< 10−4 BA

Max < 0.001

BUA

Max

LA

BA

Min

LA

BA

-

0.0192 < 0.001

LA-< 10−4---< 10−4

BA

LA

Min

< 10−4--< 10−4 LA

BUA

-

BUA

-

LA

-

< 10−5< 10−5

Min < 10−4 < 10−4

Alpha LRTC BA

LA

Min

< 10−5BABUA BUA

BUAMax

BUA BUA

Log(APP)

4

LA

HW

BUA BUA-Min

Max BUA BUA-< 10−4 LAMin< 0.001Max

0.0834 < 0.001
Min

Max BUA BUA< 0.001Max

DISCUSSION AND CONCLUSIONSIn this study, we aimed to identify the potential EEG-indicators for auditory attention. For this purpose, 64channels EEG signals of twenty-three participants were recorded under three tasks—attentive listening to lecturesin background noise, attentive and inattentive listening to background noise alone. The background noises
included multi-talker babble, highway and fluctuating traffic sounds. Three hypotheses were investigated—alphaas-inhibition, self-similarity as excitation-inhibition balance and low-frequency entrainment of brain activity byspeech envelope. Frequency and power of alpha peak, alpha-band long-range temporal correlation, and deltaabsolute power were employed as the EEG indicators for auditory attention based on these hypotheses.
The most distinct results were: (1) alpha peak frequency in left and right temporal clusters increases compared to occipital cluster during conditions that need more inhibition i.e., lecture attended in background noise
and unattended multi-talker tasks; (2) decreased overall alpha peak frequency could capture increased attentional
drop so that background unattended task in highway and fluctuating traffic exhibits the lowest alpha peak frequency compared to attentive listening tasks; (3) increased alpha peak power could reflect easier inhibition sothat alpha peak power increases during lecture attended and background unattended in highway compared toother noises and decreases during background unattended in multi-talker compared to other noises. These makesense because highway noise is the most monotonous background and multi-talker noise is the most difficult
background to inhibit. Furthermore, alpha peak power increases during background unattended in highway andfluctuating traffic compared to other tasks (easier inhibition); (4) decreased long-range temporal correlation of
the alpha activity could reflect the attentional performance so that the long-range temporal correlation is theminimum during lecture attended in silence and the maximum during lecture attended in multi-talker; (5) deltaabsolute power is able to capture speech envelope following during listening so that delta absolute power values
during lecture attended in multi-talker and fluctuating traffic noises are significantly the highest compared toother tasks with the same noises.
We revealed that overall alpha peak frequency plays two distinct roles. First, increased attentional demand ina stable state could accelerate the alpha peak frequency (e.g., attentive listening to the lecture in highway noisecompared to inattentive listening to highway noise). Second, the peak alpha frequency could be decreased due
to increased attentional demand in an unstable state when the attentional capacity is overloaded (e.g., attentive
listening to multi-talker compared to other noises). The intriguing remaining questions are whether these tworoles of alpha peak frequency could be linked to lower and upper alpha frequency and also whether they could
be observed in two distinct brain regions.
Interesting avenues of research which we are currently investigating include analyzing the inter-regional connectivity and also coupling between EEG and speech envelope as the further indicators of auditory attention.
Furthermore, analysing the cortical representation of salient events (phone ringing, car horn’s honk, and emergency siren) and the onset/offset of speech during stimuli to discover the attention-inhibition mechanism are theinteresting subjects for future studies.

7637

ACKNOWLEDGEMENTS
This study is part of DUCK project and has received research funding from Belgium Special Research
Fund (Bijzonder Onderzoeksfonds, BOF). We gratefully acknowledge the assistance and support of International
Laboratory for Brain, Music and Sound Research (BRAMS), Montreal, Canada for collecting the EEG data.

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