AUDITORY EEG SIGNAL PROCESSING (AESOP) SYMPOSIUM - SEPTEMBER 16-18, 2019 LEUVEN, BELGIUM
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Auditory
EEG Signal Processing (AESoP)
Symposium
September 16-18, 2019
Leuven, Belgium
Local Organisation
Tom Francart
Jan Wouters
Steering Committee
Alain de Cheveigné (CNRS, France)
Andrew Dimitrijevic (Sunnybrook Research Institute, Toronto, Canada)
Mounya Elhilali (Johns Hopkins, Maryland, USA)
Tom Francart (KU Leuven, Belgium)
Ed Lalor (University of Rochester, New York, USA)
Tobias Reichenbach (Imperial College London, United Kingdom)
Jonathan Simon (University of Maryland, USA)
Malcolm Slaney (Google Research, USA)
Jan Wouters (KU Leuven, Belgium)
Invited Speakers
Samira Anderson (University of Maryland, USA)
Gopala Krishna Anumanchipalli (University of California, San Francisco, USA)
Behtash Babadi (University of Maryland, USA)
Mathieu Bourguignon (BCBL, Spain)
Joachim Gross (University of Münster, Germany)
Karen Livescu (TTI-Chicago, USA)
Ross Maddox (University of Rochester, USA)
Myles Mc Laughlin (KU Leuven, Belgium)
Nima Mesgarani (Columbia University, USA)
Sarah Verhulst (Universiteit Gent, Belgium)
The symposium will be held under the auspices of
AESoP symposium, Leuven, 16–18 September 2019 iSponsors
EEG
REIMAGINED
ii AESoP symposium, Leuven, 16–18 September 2019Contents
Organizers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
Sponsors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Practical Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
Program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Speaker Abstracts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Signal Processing 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Attention Decoding 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
Attention Decoding 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
Special Populations and Applications 1 . . . . . . . . . . . . . . . . . 20
Brainstem Responses and FFR . . . . . . . . . . . . . . . . . . . . . 23
Signal Processing 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
Special Populations and Applications 2 . . . . . . . . . . . . . . . . . 30
Poster Abstracts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
AESoP symposium, Leuven, 16–18 September 2019 iiiIntroduction
Dear colleagues and friends,
It is our great pleasure to welcome you to Leuven. We hope you will not only enjoy
excellent science, but also great company, some history of our university and city,
and culinary delights.
Two years ago, when we discussed the idea or organising the first edition of the
AESoP symposium, we never thought it would be so successful as to organise a
next edition only 18 months later. The idea for this symposium was born from a
desire to bring together researchers from two fields that already interact, but could
do so even more: signal processing and neuroscience. With the wide availability
of multi-channel EEG/MEG systems and ever more powerful computers, it has be-
come possible to analyse brain signals more thoroughly than with the traditional
visual assessment of peaks in the time domain, and to do so with a single presen-
tation of natural running speech. A number of powerful quantitative methods for
EEG/MEG signal analysis are now available and under intensive further develop-
ment. The goals of the AESoP symposium are to further encourage development
of such methods, but also to make sure they are widely applied to tackle important
neuroscience questions. We therefore want to bring together engineers and neuro-
scientists (or any combination within the same person), for three days of intense
scientific exchange.
To stimulate discussion, we’ve allowed time for questions after each presentation,
plenty of breaks, and long poster sessions. The posters are an integral part of the
program, so we highly encourage viewing them all.
I would like to express my sincere thanks to everyone who contributed to the organ-
isation of this symposium: the steering committee, the enthusiastic Leuven team,
the invited speakers, and of course all participants for making this a success.
Yours sincerely,
Tom Francart,
supported by the steering committee:
Jan Wouters, Alain de Cheveigné, Andrew Dimitrijevic, Mounya Elhilali, Ed Lalor,
Tobias Reichenbach, Malcolm Slaney, Jonathan Simon
AESoP symposium, Leuven, 16–18 September 2019 1Practical Information
Posters
To guarantee the smooth running of the poster sessions, we ask you to put up your
poster at the designated number as soon as you arrive. This number can be found
in the program book. Poster panels are 2 m high and 1 m wide. Tuesday after the
poster session, we kindly ask you to remove your poster.
• The authors of odd-numbered posters should be present at their poster during
the Monday poster session.
• The authors of even-numbered posters should be present at their poster during
the Tuesday poster session.
Presentations
As a speaker, you are requested to upload your presentation no later than during
the break preceding your session (see program). Speakers are kindly requested to
respect their presentation time to allow time for questions and keep on schedule.
Airport
To reach the airport from the symposium location you can take bus 2 (direction
Kessel-Lo) from bus stop “Leuven Sint-Michielskerk”. Alternatively you can take
one of the buses: 3, 4, 5, 6, 8, 9, 284, 285, 315, 316, 333, 352, 358, 370, 371, 373,
380, 395 from bus stop “Leuven Rector De Somerplein zone A”. In all cases you
need to get off at stop “Leuven Station” from where you can take train to Brussels
Airport—Zaventem (cost 9,30 EUR including Diabolo fee).
A bus ticket, valid for 60 minutes, costs 3 EUR and can be bought on the bus (or
cheaper from a machine or shop).
Alternatively, you can take a 20 min walk to from the symposium venue to the train
station.
2 AESoP symposium, Leuven, 16–18 September 2019Program
Monday, September 16
11:00 Registration
12:00 - 13:00 Lunch
13:00 - 13:05 Welcome, Tom Francart
13:05 - 14:05 Signal Processing 1
Moderator: Jonathan Simon
13:05 Alain de Cheveigné, Tutorial: making the most of the data
with linear methods
13:35 Behtash Babadi, Neuro-current response functions: a uni-
fied approach to MEG source analysis under the continuous
stimuli paradigm
14:05 - 14:35 Break
14:35 - 16:25 Deep Learning
Moderator: Malcolm Slaney
14:35 Malcolm Slaney, The problems with attention decoding
15:05 Karen Livescu, Deep multi-view representation learning: old
and new approaches inspired by canonical correlation analysis
15:35 Greg Ciccarelli, A neural network training approach for high
accuracy, low latency auditory attention decoding
15:55 Gopala Krishna Anumanchipalli, Physiology grounded deep
networks for decoding speech and language from the brain
16:25 - 18:25 Poster Session
AESoP symposium, Leuven, 16–18 September 2019 3Tuesday, September 17
9:00 - 10:30 Attention Decoding 1
Moderator: Alain de Cheveigné
9:00 Nima Mesgarani, Hierarchical encoding of attended auditory
objects in multi-talker speech perception
9:30 Ali Aroudi, Binary-masking-based auditory attention decod-
ing without access to clean speech signals
9:50 Emina Alickovic, An overview of auditory attention decipher-
ing methods
10:10 Simon Geirnaert, A comparative study of auditory attention
decoding algorithms
10:30 - 11:00 Break
11:00 - 12:00 Attention Decoding 2
Moderator: Alain de Cheveigné
11:00 Cantisani Giorgia, Decoding auditory attention in polyphonic
music based on EEG: a new dataset and a preliminary study
11:20 Emily Graber, The focus of attention in music modulates
neural responses
11:40 Lars Hausfeld, Cortical processing of distractor voices in nat-
ural auditory scenes depends on perceptual load
12:00 - 13:00 Lunch
13:00 - 14:25 Special Populations and Applications 1
Moderator: Jan Wouters
13:00 Jan Wouters, Introduction
13:05 Mathhieu Bourguignon, Speech tracking in noise: develop-
ment from childhood to adulthood
13:35 Samira Anderson, Age-related deficits in neural processing
revealed for specific temporal components of speech
14:05 Ben Somers, Temporal response functions in cochlear implant
users
14:25 - 14:55 Break
continued on the next page
4 AESoP symposium, Leuven, 16–18 September 201914:55 - 16:35 Brainstem Responses and FFR
Moderator: Tobias Reichenbach
14:55 Tobias Reichenbach, Introduction
15:05 Sarah Verhulst, Using computational models to improve the
diagnostic sensitivity of auditory brainstem EEG
15:35 Ross Maddox, Measuring the frequency-specific auditory
brainstem response to naturalistic speech
16:05 Jonathan Simon, High frequency cortical processing of con-
tinuous speech in younger and older listeners
16:35 - 18:35 Poster Session
19:00 Dinner, symposium venue
AESoP symposium, Leuven, 16–18 September 2019 5Wednesday, September 18
9:00 - 10:40 Signal Processing 2
Moderator: Tom Francart
9:00 Joachim Gross, Studying processing of continuous audiovisual
speech with MEG
9:30 Christoph Daube, Slow and fast components in MEG re-
sponses to naturalistic speech
10:00 Maryam Faramarzi Yazd, EEG correlates of illusory auditory
perception
10:20 Ehsan Darestani Farahani, Source modeling of auditory
steady-state responses using minimum-norm imaging
10:40 - 11:10 Break
11:10 - 12:30 Special Populations and Applications 2
Moderator: Andrew Dimitrijevic
11:10 Octave Etard, Neural speech tracking in the delta and theta
frequency bands differentially encodes comprehension and in-
telligibility of speech in noise
11:40 Myles McLaughlin, Transcranial alternating current stimu-
lation: mechanisms and future directions
12:10 Melissa Polonenko, Optimal parameters for obtaining robust
parallel auditory brainstem responses at different intensities
12:30 - 12:40 Final Notes & Best Poster Award, Tom Francart
12:40 - 14:00 Lunch
14:00 End Symposium
6 AESoP symposium, Leuven, 16–18 September 2019Speaker Abstracts
Monday, September 16: Signal Processing 1
13:05–14:05 Moderator: Jonathan Simon
Tutorial: making the most of the data with linear methods
Alain de Cheveigné (1,2)
(1) CNRS, École normale supérieure (University Paris Sciences Lettres); (2) UCL
EEG and MEG both record signals on multiple channels, electric or magnetic,
each of which mirrors the activity of billions of neural sources. The mixing pro-
cess is thought to be linear: each channel is the weighted sum of neural (and noise)
sources. It is thus no surprise that EEG and MEG data are most often analyzed
using linear methods involving spatial or temporal filters. Given the ubiquity of
linear operations, it is useful to reason in terms of the vector spaces spanned by the
data. The N time series recorded by the device together span a space of dimension
N, containing all their linear combinations, itself a subspace of the billion-dimension
space of neural activity. This tutorial discusses analysis methods (such as PCA,
ICA, CCA, etc.) in terms of their ability to find useful projections within these
spaces and subspaces. An important concept is dimensionality. Dimensionality de-
termines both the ability of a linear transform to separate signal from noise, and the
tendency to overfit of the data-driven methods that are used discover such trans-
forms. Greater dimensionality (more channels, time shifts, etc.) increases resolution
power. Lesser dimensionality (channel selection, PCA followed by truncation, etc.)
reduces overfitting. Progress in data analysis depends on finding an optimal path
between these conflicting requirements. I will review a number of ideas that may
help us move forward, to make the most of the data that we can record from the
brain.
AESoP symposium, Leuven, 16–18 September 2019 7Neuro-current response functions: a unified approach to MEG source analysis under the continuous stimuli paradigm Proloy Das (1,2), Christian Brodbeck (2), Jonathan Z. Simon (1,2,3), Behtash Babadi (1,2) (1) Department of Electrical & Computer Engineering, University of Maryland; (2) Institute for Systems Research, University of Maryland; (3) Department of Biology, University of Maryland Existing work suggests that certain features of speech, such as the acoustic en- velope, can be used as reliable linear predictors of the neural response manifested in MEG/EEG. The corresponding linear filters are referred to as temporal response functions (TRFs). While the functional roles of specific components of the TRF are well-studied, the cortical origins of the underlying neural processes are not as well understood. Existing methods for obtaining cortical representations of the TRF work in a two-stage fashion: either the TRFs are first estimated at the sensor level, and then mapped to the cortex via source localization, or the data are first mapped to the cortex followed by estimating TRFs for each of the resulting cortical sources. Given that each stage is biased towards specific requirements, the end result typ- ically suffers from destructive propagation of biases. In this work we address this issue by introducing Neuro-Current Response Functions (NCRFs), which are three- dimensionally oriented linear filters distributed throughout the cortex, that predict the cortical current dipoles giving rise to the observed MEG (or EEG) data in re- sponse to speech. We present a fast estimation algorithm and demonstrate its utility through application to simulated and real MEG data under auditory experiments. Our results show significant improvements over existing work, in terms of both spa- tial resolution and reliance on post-processing steps such as clustering and denoising. Acknowledgements: This work was supported by the National Science Foundation (Award No. 1552946 and 1734892), the Defense Advanced Research Projects Agency (Award No. N6600118240224) and the National Institutes of Health (Award No. R01-DC014085). 8 AESoP symposium, Leuven, 16–18 September 2019
Monday, September 16: Deep Learning
14:35–16:25 Moderator: Malcolm Slaney
The problems with attention decoding
Malcolm Slaney (1)
(1) Google Machine Hearing Research
I would like to review the status of attention decoding, and open a discussion about
the interesting scientific and engineering problems that remain. Attention decoding
is popular enough to be considered for real-life applications and here the engineer-
ing and machine-learning problems rule. But there are also a lot of questions about
how the brain processes sound and how to model attention. I would like to start a
discussion about the next steps, some of which I hope will be answered in the talks
to follow.
AESoP symposium, Leuven, 16–18 September 2019 9Deep multi-view representation learning: old and new ap- proaches inspired by canonical correlation analysis Karen Livescu (1) (1) TTI-Chicago Many scientific and engineering applications involve settings where we have access to multiple types of sensor data, or multiple “views”, corresponding to the same phenomenon. For example, we may have access to audio, video, and physiological measurements of a person speaking. In such settings, it is often possible to learn better representations (features) of each view by taking advantage of the statistical relationships between the multiple views. One classic approach for learning linear projections of two views is canonical correlation analysis (CCA). However, for many types of data it is preferable to learn non-linear features. This talk will review several approaches, including deep and variational CCA, for learning non-linear representa- tions in a multi-view setting. It will also include new extensions tailored for sequence data. Most of the experiments will be on speech recognition, but the techniques are broadly applicable to any multi-view learning setting. 10 AESoP symposium, Leuven, 16–18 September 2019
A neural network training approach for high accuracy, low
latency auditory attention decoding
G. Ciccarelli (1), S. Haro (2), C. Calamia (1), M. Brandstein (1), T.
Quatieri (1,2), C. Smalt (1)
(1) Bioengineering Systems and Technologies Group, MIT Lincoln Laboratory, Lexington; (2) Speech
and Hearing Bioscience and Technology, Harvard Medical School, Boston
Auditory attention decoding (AAD) is the process of determining to which acoustic
source a listener desires to attend. High accuracy and low latency decoding is a key
requirement of a brain computer interface based hearing aid or hearing enhance-
ment device. To date, research into noninvasive, wearable electroencephalography
(EEG) AAD has concentrated primarily on improving decoding accuracy and under-
standing how decoding accuracy declines with smaller segments of evaluation data.
However, developing techniques to improve response time to attention switching
in an acoustic environment with multiple simultaneous speakers (the cocktail party
problem) is just as important as improving decoding accuracy. In this work, we show
how artificially introducing attention switches during training cannot only increase
response times but also improve accuracy relative to standard training procedures.
Our highest decoding accuracy is 81 % with a 3.75 s attentional switching latency
compared to a baseline system of 79 % with a 5.03 s latency. Our most responsive
system has an accuracy of 68 % and latency of 1.19 s compared to a baseline sys-
tem accuracy of 63 % and latency of 1.24 s. Future work will evaluate this training
paradigm with directed and at-will switches of auditory attention.
Acknowledgements: This material is based upon work supported by the Under
Secretary of Defense for Research and Engineering under Air Force Contract No.
FA8702-15-D-0001.
AESoP symposium, Leuven, 16–18 September 2019 11Physiology grounded deep networks for decoding speech and language from the brain Gopala K. Anumanchipalli (1), Josh Chartier (1), Pengfei Sun (1), Ed- ward F. Chang (1) (1) University of California, San Francisco Spoken communication is basic to who we are. Neurological conditions that re- sult in loss of speech can be devastating for affected patients. This work will sum- marize recent efforts in decoding neural activity directly from the surface of the speech cortex during fluent speech production, monitored using intracranial electro- corticography (ECoG). Decoding speech from neural activity is challenging because speaking requires very precise and rapid multi-dimensional control of vocal tract ar- ticulators. We first describe the articulatory encoding characteristics in the speech motor cortex and compare them against other representations like the phonemes. We then describe deep learning approaches to convert neural activity into these articulatory physiological signals that can then be transformed into audible speech acoustics or decoded to text. In closed vocabulary tests, listeners could readily identify and transcribe speech synthesized from cortical activity. We show that the described biomimetic strategies make optimal use of available data; generalize well across subjects, and also demonstrate silent speech decoding. These results set a new benchmark in the development of high performance Brain-Computer Interfaces for assistive communication in paralyzed individuals with intact cortical function. 12 AESoP symposium, Leuven, 16–18 September 2019
Tuesday, September 17: Attention Decoding 1
9:00–10:30 Moderator: Alain de Cheveigné
Hierarchical encoding of attended auditory objects in multi-
talker speech perception
Nima Mesgarani (1,2), James O’Sullivan (1,2)
(1) Zuckerman Mind Brain Behavior Institute, Columbia University; (2) Electrical Engineering De-
partment, Columbia University
Humans can easily focus on one speaker in multi-talker acoustic environments. How
different areas in the human auditory cortex (AC) represent the acoustic components
of mixed speech is unknown. We recorded invasively from primary and nonprimary
AC in neurosurgical patients as they listened to multi-talker speech. We found
that neural sites in primary AC responded selectively to the individual speakers
in the mixture and were relatively unchanged with attention. In contrast, neural
sites in nonprimary AC were less discerning of individual speakers but selectively
represented the attended speaker. Moreover, the encoding of the attended speaker
in nonprimary AC was insensitive to the varying degree of acoustic overlap with
the unattended speaker. Finally, this emergent representation of attended speech
in nonprimary AC was linearly predictable from the responses in primary AC. Our
results reveal the neural computations underlying the hierarchical formation of au-
ditory objects in human AC during multi-talker speech perception.
AESoP symposium, Leuven, 16–18 September 2019 13Binary-masking-based auditory attention decoding without access to clean speech signals Ali Aroudi (1), Hendrik Kayser (1), Simon Doclo (1) (1) Department of Medical Physics and Acoustics and Cluster of Excellence “Hearing4all”, University of Oldenburg Objectives: During the last decade several EEG-based auditory attention decoding (AAD) methods have been proposed, which often require the clean speech signals of the speakers as reference signals for decoding. However, in many applications these clean speech signals are obviously not available in practice. In this contribution we aim at generating appropriate reference signals for decoding from noisy and rever- berant microphone signals in an acoustic scene with two competing speakers. Methods: We propose a reference signal generation approach based on binary mask- ing, which disregards the intervals mainly containing interfering speech and back- ground noise. The binary masks are determined from the directional speech presence probability of both speakers, which is estimated from the microphone signals. The reference signals are generated by either masking the microphone signals or the out- put signals of an MVDR beamformer. In addition, the estimated binary masks are directly considered as reference signals for decoding. Conclusions: The simulation results show that the proposed binary-masking-based approach significantly improves the decoding performance (especially in the rever- berant condition) compared to using the (non-masked) microphone and MVDR output signals. In addition, the results show that directly using the estimated bi- nary masks as reference signals yields a decoding performance that is comparable to using the masked MVDR output signals. Acknowledgements: This work was supported by the Deutsche Forschungsgemein- schaft (DFG, German Research Foundation)—Project ID 390895286 EXC 2177/1. 14 AESoP symposium, Leuven, 16–18 September 2019
An overview of auditory attention deciphering methods
Emina Alickovic (1,2), Thomas Lunner (1,2,3,4), Fredrik Gustafsson (1),
Lennart Ljung (1)
(1) Linkoping University, Department of Electrical Engineering; (2) Oticon A/S, Eriksholm Research
Centre; (3) Technical University of Denmark, Department of Health Technology, Hearing Systems;
(4) Linkoping University, Swedish Institute for Disability Research, Linnaeus Centre HEAD
Speech communication in everyday listening environments often requires selective
attending to a particular talker amid a crowd of competing talkers and other un-
wanted sounds. Auditory attention deciphering (AAD) methods attempt to deci-
pher which of the talkers received the listener’s attention in such complex envi-
ronments by analyzing electrophysiological data—measured brain activity elicited
during selective listening. The main focus in AAD methods is on the linear re-
lations between electrophysiological and speech data. We present an overview of
the recent trends in multivariate correlation-based and model-based learning frame-
works that have been developed in recent decades as AAD methods to expose the
talker of a listener’s interest. The two frameworks compute these linear relations in
different ways. Whereas canonical correlation analysis (CCA)—a representative of
correlation-based framework—finds two different linear subsets, one that best cor-
relates electrophysiological to speech data and one that best correlates speech to
electrophysiological data, encoding and decoding approaches—two representatives
of model-based framework—focus on finding only one of these two subsets. Similar-
ities and differences between these frameworks together with different approaches
to estimate these subsets, for example, sparse vs. dense estimation, are discussed
so as to provide better understanding and practical use of state-of-the-art methods
available in the literature.
AESoP symposium, Leuven, 16–18 September 2019 15A comparative study of auditory attention decoding algo- rithms Simon Geirnaert (1,2), Servaas Vandecappelle (2,1), Tom Francart (2), Alexander Bertrand (1) (1) ESAT-STADIUS, Department of Electrical Engineering, KU Leuven; (2) Research Group Ex- pORL, Department of Neurosciences, KU Leuven Hearing aids and cochlear implants have major difficulties when operating in a so- called cocktail party scenario. It is not only essential to separate a recorded mixture of the speech or audio signals in its original contributions, but also to determine to which speech signal the hearing aid user intends to listen. Recently, there has been a growing interest in this second problem. In particular, it has been shown that the brain synchronizes with the envelope of the attended speech signal. This creates the possibility to decode the attention directly from the brain activity, recorded by, e.g., electroencephalography sensors, which has lead to the development of different auditory attention decoding (AAD) algorithms. In this study, we evaluate different AAD algorithms on two independent datasets and compare and interpret the AAD algorithms using, among others, a recently designed new metric (MESD). We take their various characteristics into account, for example, by comparing linear (CCA, regression-based, …) with nonlinear ((deep) neural networks) algorithms, backward with hybrid models, different regularization techniques (ridge, sparse, …), etc. Acknowledgements: This research is funded by an Aspirant Grant from the Research Foundation Flanders (FWO) (for S. Geirnaert), the KU Leuven Special Research Fund C14/16/057, FWO project nr. G0A4918N, the European Research Council (ERC) under the H2020 programme (No. 802895 and No. 637424). 16 AESoP symposium, Leuven, 16–18 September 2019
Tuesday, September 17: Attention Decoding 2
11:00–12:00 Moderator: Alain de Cheveigné
Decoding auditory attention in polyphonic music based on
EEG: a new dataset and a preliminary study
Giorgia Cantisani (1), Slim Essid (1), Gaël Richard (1)
(1) LTCI, Télécom Paris, Institut Polytechnique de Paris
In this work, we address the problem of EEG-based decoding of auditory attention
to a target instrument in realistic polyphonic music, i.e. recorded music, featuring
two or three instruments played concurrently. To this end, we exploit a stimulus
reconstruction model able to reconstruct an audio representation of the attended
instrument from single-trial multi-channel EEG recordings. This model was proven
to decode successfully the attention to speech in multi-speaker environments but, to
our knowledge, was never applied to musical stimuli. For this purpose, we acquired a
new dataset, named MAD-EEG, which consists of 20-channel EEG signals recorded
from 8 subjects listening to solo, duo and trio music excerpts and attending to one
pre-specified instrument in the mixture. It is worth noting that these stimuli were
not specifically designed to elicit ERPs, and were played to the subjects using loud-
speakers instead of headphones, rendering different spatial configurations. We were
thus able to investigate how the decoding performance is influenced by properties of
the musical stimuli, such as number and type of instruments, spatial rendering, mu-
sic genre, and the melody/rhythmical pattern that is played. The obtained results
are comparable to those obtained on speech data in previous works, and confirm
that it is thus possible to correlate the human brain’s activity with musically rele-
vant features of the attended source.
Acknowledgements: This project has received funding from the European Union’s
Horizon 2020 research and innovation program under the Marie Skłodowska-Curie
grant agreement No. 765068.
AESoP symposium, Leuven, 16–18 September 2019 17The focus of attention in music modulates neural responses Emily Graber (1), Natalie Nguyen (1), Andrew Dimitrijevic (1) (1) Sunnybrook Research Institute, Toronto Objectives: Music is a complex stimulus often comprised of simultaneous voices with multiple features per voice. Here, we used EEG to investigate how a listener’s focus of attention to one or another musical voice in a duet changes the brain’s representation of the voice/features under attention. Methods: 18 NH volunteers, self-reportedly non-musicians, listened to 14 two-part Bach Inventions passively, while watching a movie, and actively, while attending to a specific musical voice. Multivariate temporal response function (TRF) encoders were computed using the time-aligned features of pitch, meter, amplitude, and on- sets for both musical voices during passive and active listening. Encoders were compared between passive and active listening, and under active attention to the upper and lower musical voices. Prediction accuracy will also be evaluated with attended/unattended features separately under the hypothesis that attended ones best model the EEG data. Conclusions: Preliminary analyses suggest that active listening to any musical voice increases the TRF amplitudes compared to passive listening. Currently, in our NH participants, effects of attention are most evident in TRFs to the amplitude en- velopes. Whether a similar relationship holds for participants with hearing loss can be explored in the future. Overall, these data demonstrate the feasibility of using musical stimuli in an attention paradigm. Acknowledgements: Support provided by Oticon Medical. 18 AESoP symposium, Leuven, 16–18 September 2019
Cortical processing of distractor voices in natural auditory
scenes depends on perceptual load
Lars Hausfeld (1,2), Martha Shiell (1,2), Elia Formisano (1,2,3), Lars
Riecke (1,2)
(1) Department of Cognitive Neuroscience, Maastricht University; (2) Maastricht Brain Imaging
Centre (M-BIC), The Netherlands; (3) Maastricht Centre for Systems Biology, Maastricht Univer-
sity
Objectives: Selective attention is essential for processing multi-speaker auditory
scenes. It has been proposed that for visual selective attention the depth of process-
ing distractors is determined by the perceptual load of the stimulus. Whether this
“load dependency” exists in the auditory system is unclear.
Methods: We hypothesized that higher perceptual load would reduce the processing
resources available for the segregation of distractor sounds.
Participants (N = 20) were presented with auditory scenes including 3 speakers and
asked to selectively attended to one speaker. We varied perceptual load by spa-
tial cues (ITD = 0 and ITD 6= 0). EEG data was predicted by temporal response
functions including envelopes of the target speaker and either averaged or individ-
ual distractor speakers (AD or ID, respectively). We reasoned that hypothesized
reduced segregation for ITD = 0, would be reflected by higher differences in model
performance between AD and ID vs. ITD 6= 0.
Results: Conform with our hypothesis, results show that performance differences
between AD and ID models were significantly higher during ITD 6= 0 vs. ITD = 0
conditions. This effect was strongest in the theta band and at early delays (0–
200ms).
Conclusions: Our results indicate that during lower perceptual load, the cortex
represents individual distractor speech signals as more segregated. This suggests
that, in addition to purely acoustical properties, the processing of multiple distrac-
tor speakers depends on perceptual load.
Acknowledgements: This work was supported by the Netherlands Organisation for
Scientific Research (NWO; VENI grant 451-17-033 to L.H.).
AESoP symposium, Leuven, 16–18 September 2019 19Tuesday, September 17: Special Populations and Applications 1 13:00–14:25 Moderator: Jan Wouters Speech tracking in noise: development from childhood to adulthood Mathieu Bourguignon (1,2,3) (1) Laboratoire de Cartographie fonctionnelle du Cerveau, UNI-ULB Neuroscience Institute, Univer- sité libre de Bruxelles; (2) Laboratoire Cognition, Langage et Développement, UNI-ULB Neuroscience Institute, Université libre de Bruxelles; (3) BCBL, Basque Center on Cognition, Brain and Language, Spain During connected speech listening, oscillatory activity within auditory cortices tracks speech rhythmicity at syllable (4–7 Hz) and phrasal (below 1 Hz) rates. In adults, such speech brain tracking (SBT) is also observed in speech-in-noise (SiN) condi- tions, and in that context, oscillatory brain activity aligns more to the attended speech than to the heard sound. This talk will present recent studies relying on SBT to better understand 1) the neuronal basis of the well-described difficulty of children to perceive SiN, 2) the impact of noise properties and audiovisual integra- tion on SiN perception, and 3) the relation between SiN perception and reading abilities. This research shows that tracking of SiN at phrasal and syllable rates parallels well documented behavioral effects: SBT and SiN perception (i) are more compromised by informational than non-informational masking, (ii) are enhanced when seeing lip movements, and (iii) increase from age 6 to 12. Our results also revealed substantial differences between SBT at phrasal and syllable rates: both maturation and seeing lip movements boosted syllable tracking globally and phrasal tracking selectively in informational noise conditions. Finally, these effects are behaviorally relevant since the ability to maintain SBT at phrasal rate in SiN conditions and to leverage visual speech information to boost it directly relate to reading abilities. Acknowledgements: Mathieu Bourguignon is supported by the program Attract of Innoviris (grant 2015-BB2B-10), by the Marie Skłodowska-Curie Action of the European Commission (grant 743562) and by the Spanish Ministry of Economy and Competitiveness (grant PSI2016-77175-P). 20 AESoP symposium, Leuven, 16–18 September 2019
Age-related deficits in neural processing revealed for specific
temporal components of speech
Samira Anderson (1)
(1) University of Maryland, Department of Hearing and Speech Sciences
Older adults often report that they have trouble with clarity rather than with loud-
ness when conversing with others. This lack of clarity may stem from decreased
neural processing of the temporal cues that help to distinguish one word from an-
other. Identification of neural processing deficits for specific temporal components
of speech may assist in developing management strategies for older adults who strug-
gle to hear. To that end, we recorded the frequency-following response (FFR) to
words that are distinguished by temporal duration cues and analyzed responses in
the time-frequency domain. We recruited normal-hearing middle-aged (55–70 yrs)
and young adult participants (18–30 yrs) and recorded FFRs to word pairs differing
in vowel duration (WHEAT vs. WEED) and consonant-transition duration (BEAT
vs. WHEAT). We also recorded auditory brainstem responses (ABRs) in quiet
and noise to obtain measures of auditory nerve and early brainstem function. We
found that age-related deficits were more pronounced for the word containing the
longer vowel duration (WEED) and for the word containing the shorter consonant
transition duration (BEAT). ABR measures of early neural function contributed to
variance in the neural representation of these temporal components, but only in
the middle-aged participants. These results have implications for management, and
efforts are underway to determine if training on the perception of these temporal
components leads to improved speech understanding.
Acknowledgements: This research was supported by the National Institute on Deaf-
ness and Other Communication Disorders of the National Institutes of Health under
Award Number R21DC015843.
AESoP symposium, Leuven, 16–18 September 2019 21Temporal response functions in cochlear implant users Ben Somers (1), Eline Verschueren (1), Tom Francart (1) (1) Research Group ExpORL, Department of Neurosciences, KU Leuven Objective: EEG recorded while listening to natural running speech can be used to estimate the Temporal Response Function (TRF), which models the encoding of the speech envelope in the listener’s brain signals. The computation of TRFs in cochlear implant (CI) users is complicated because of the electrical stimulation artifacts, which are highly correlated with the speech envelope and distort TRFs. The objective of this study is to compute TRFs in CI users, and assess the effect of electrical stimulation artifacts and their removal on the TRFs. Methods: EEG was measured from CI users while they listened to natural run- ning speech. A previously-validated CI artifact removal technique was applied, in which short gaps were inserted in the speech stimulus to create artifact-free EEG samples. TRFs were computed using the resulting EEG and speech envelope, and were compared with conditions where no CI artifact removal was applied. Conclusions: Typical TRF morphologies were found for both artifact and artifact- free conditions. Without CI artifact removal, TRF coefficients at short latencies were dominated by artifacts, rendering these TRFs useless as a model to predict EEG. This study demonstrates for the first time the possibility to compute TRFs in CI users, and stresses the need for adequate CI artifact removal. TRFs and changes in their morphologies can be used as an objective measure in future studies to eval- uate encoding of speech in challenging listening conditions. Acknowledgements: ERC H2020 grant No. 637424, and FWO PhD grants 1S46117N and 1S86118N. 22 AESoP symposium, Leuven, 16–18 September 2019
Tuesday, 17 September: Brainstem Responses and
FFR
14:55–16:35 Moderator: Tobias Reichenbach
Using computational models to improve the diagnostic sen-
sitivity of auditory brainstem EEG
Sarah Verhulst (1)
(1) Ghent University, Department of Information Technology
Auditory brainstem EEG has a long history in the diagnosis of sensorineural or
central hearing deficits, but because its dominant sources are generated in periph-
eral/brainstem structures, they offer only an indirect quantification of the underly-
ing hearing deficit. Head size, background noise, cochlear mechanics, and various
hearing deficits can all impact response amplitude and hence require (i) differential
paradigms in which confounding factors are minimized, and (ii), a thorough under-
standing of the transformation between the stimulus and the measured response.
Here, I will discuss how computational models of the auditory periphery can be
used to develop robust and sensitive EEG paradigms to isolate different aspects
of sensorineural hearing loss. Both stimulus characteristics and recording analysis
techniques are introduced and discussed on the basis of recordings from normal-
hearing and hearing-impaired listeners.
Acknowledgements: Work supported by the European Research Council (RobSpear;
678120).
AESoP symposium, Leuven, 16–18 September 2019 23Measuring the frequency-specific auditory brainstem response to ongoing naturalistic speech R. K. Maddox (1-3), M. J. Polonenko (2,3) (1) University of Rochester, Department of Biomedical Engineering; (2) University of Rochester, Department of Neuroscience; (3) University of Rochester, Del Monte Insitute for Neuroscience Objectives: 1) To measure the auditory brainstem reponse to natural speech in a way that yields canonical waveforms, 2) To use speech to separately assess hearing across frequency bands. Methods: Natural speech was resynthesized so that during periodic vowel segments the phase of the harmonics were aligned so that they all crossed zero once per period, leading to highly peaky waveforms. This was accomplished by extracting the fun- damental frequency of the speech and synthesizing the harmonics with the desired phase. The time-varying amplitude of each harmonic was taken from the spectro- gram of the original speech. The result was a stimulus whose spectrogram was nearly identical to the original speech, but was comprised of a series of pulses with known times. These pulse times were then used to create an impulse train regressor and derive the response waveform through deconvolution with the recorded EEG. This process was extended to measuring separate responses across frequency bands by resynthesizing speech several times at slightly different fundamental frequencies and then filtering. Single-band responses were computed by deconvolution with each of the bands’ respective pulse trains. Conclusions: Natural speech can be processed and resyenthesized to elicit canonical ABR waveforms with all standard component waves, with application in under- standing the role of distinct subcortical nuclei in cognitive processes. This can be extended for multi-band audiological purposes. Acknowledgements: National Institute for Deafness and other Communication Dis- orders, R00DC014288. 24 AESoP symposium, Leuven, 16–18 September 2019
High frequency cortical processing of continuous speech in
younger and older listeners
Joshua P. Kulasingham (1), Christian Brodbeck (2), Alessandro Pre-
sacco (2), Stefanie E. Kuchinsky (5), Samira Anderson (3), Jonathan Z.
Simon (1,2,4)
(1) Department of Electrical and Computer Engineering; (2) Institute for Systems Research; (3)
Department of Hearing and Speech Sciences; (4) Department of Biology, University of Maryland; (5)
Audiology and Speech Pathology Center, Walter Reed National Military Medical Center
Neural processing along the ascending auditory pathway shows a progressive re-
duction in frequencies. The frequency-following response (FFR) of midbrain, from
electroencephalography (EEG), is dominated by time-locked responses from ∼100
to several hundred Hz. In contrast, cortical speech responses, from EEG or mag-
netoencephalography (MEG), are dominated by time-locked responses of a few to
tens of Hz. However, fast (∼100 Hz) MEG responses time-locked to fast envelope
changes in speech have been reported. We investigate such MEG responses to con-
tinuous speech using neural source-localized temporal response functions (TRF)
analysis. Continuous speech stimuli were presented to 40 subjects (17 younger) and
fast MEG responses were analyzed. The spatiotemporal response profile indicates a
predominantly cortical origin with ∼35 ms latency and right hemisphere bias. TRF
analysis was performed using the: a) 70–300 Hz band of the waveform/carrier, and
b) 70–300 Hz envelope of the high frequency (300–4000 Hz) band; the latter was seen
to dominate. Age-related differences were analyzed to investigate a previously seen
reversal, whereby older listeners have weaker midbrain FFR responses, but, para-
doxically, stronger low frequency cortical responses. In contrast, this study found
no such age-related differences in high frequency cortical responses. In conclusion,
FFR-like cortical responses share properties with both midbrain at the same fre-
quencies and cortex at much lower frequencies.
Acknowledgements: Funding for this study was provided by the National Insti-
tute on Deafness and Other Communication Disorders (R01-DC014085), the Na-
tional Institute of Aging (P01-AG055365), and the National Science Foundation
(SMA1734892).
AESoP symposium, Leuven, 16–18 September 2019 25Wednesday, September 18: Signal Processing 2 9:00–10:40 Moderator: Tom Francart Using MEG to study the dynamics of information process- ing in the human brain Joachim Gross (1,2) (1) University of Muenster, Institute for Biomagnetism and Biosignalanalysis; (2) University of Glasgow, Centre for Cognitive Neuroimaging There is a growing number of studies demonstrating a temporal reorganization of human brain oscillations in response to complex quasi-rhythmic stimuli such as speech. The reorganization is characterized by a temporal alignment of frequency- specific brain activity to stimulus features. However, the differential contributions of bottom-up and top-down processes to this alignment have remained largely un- known. Furthermore, we are just beginning to understand what physical stimulus features and what linguistic structures are entraining brain activity. Recent studies suggest that this entrainment reflects cognitive processes of temporal coding, seg- mentation and prediction that are orchestrated by hierarchically organized brain oscillations. In my presentation I will give an overview of our recent studies in this field and present new developments. I will discuss how rhythmic brain activity could support the processing of complex, naturalistic stimuli and, ultimately, facilitates human communication. 26 AESoP symposium, Leuven, 16–18 September 2019
Slow and fast components in MEG responses to naturalistic speech Christoph Daube (1), Joachim Gross (1,2), Robin A. A. Ince (1) (1) University of Glasgow, Institute of Neuroscience and Psychology; (2) University of Muenster, Institute of Biomagnetism and Biosignalanalysis Over the recent years, countless studies have demonstrated that non-invasive record- ings of brain responses to naturalistic speech constitute a rich experimental setup. Often, such investigations focus on the low-frequency portion of the recorded signal and explore how different features of the stimulus explain these responses. Con- sistent effects for other parts of the neuronal responses are however more rarely reported. Using an information theoretic approach building on Transfer Entropy (TE), we here report evidence of a coupling signature at faster frequencies. In TE, the idea is to describe the delayed relationship between a source and a target variable while accounting for auto-correlations in the target. We find that in simplis- tic scenarios with spectrally located and strongly auto-correlated signals, classic TE formulations fail in recovering simulated effect sizes and delays. The combination of a time-delayed non-uniform embedding and local conditional entropy however mitigates these problems. Applying it to a passive story-listening MEG dataset, we first find the known low-frequency (
EEG correlates of illusory auditory perception Maryam Faramarzi Yazd (1,2), André Aleman (2), Branislava Ćurčić- Blake (2), Christoph S. Herrmann (1) (1) Experimental Psychology Laboratory, University of Oldenburg; (2) University Medical Center Groningen Objectives: We employed an auditory signal detection task to investigate the neural markers in electroencephalogram (EEG) recordings corresponding to illusory voice perception and compare it to neural correlates of detecting near-threshold voices in noise. More specifically, we contrasted hits to trials classified as false alarms which allowed us to investigate the neural mechanisms that are at least in part indepen- dent of the acoustic properties of the external auditory stimuli. Methods: In order to address this question, we recorded EEG in 21 normal-hearing participants while they were performing an adapted speech recognition in noise (SRN) task for 30 minutes, composed of 180 randomly presented 1s speech snippets at near threshold intensity masked by a continuous steady state speech-shaped noise. Conclusions: Neuro-oscillatory responses corresponding to illusory perceptions can- not be fully captured by ERPs due to the absence of external stimuli and lack of information in the exact onset of elicited perceptual processes. Event-related spec- tral perturbation (ERSP) suffers less from this ambiguity. The performed analysis revealed an early fronto-central lower theta subband and a later midparietal delta power enhancement in hits. A similar pattern, although less prominently, was also found in false alarms suggesting that the observed neural activity leading to the conscious perception can be elicited in both absence and presence of the external auditory stimuli. 28 AESoP symposium, Leuven, 16–18 September 2019
Source modeling of auditory steady-state responses using
minimum-norm imaging
Ehsan Darestani Farahani (1), Jan Wouters (1), Astrid van Wieringen (1)
(1) Research Group ExpORL, Department of Neurosciences, KU Leuven
Objectives: As auditory steady-state responses (ASSRs) have a wide range of clini-
cal and research applications, there is a great interest to investigate the underlying
neural generators. The main sources of ASSRs are located bilaterally in the audi-
tory cortex (AC), although some studies avoiding prior assumptions regarding the
number and location of the sources have also reported activity of sources outside
the AC. However, little is known about the number and location of these sources.
We present a novel extension to minimum-norm imaging (MNI) which facilitates
ASSR source reconstruction and provides a comprehensive and consistent picture of
sources.
Methods: MNI was applied to the preprocessed EEG data and a source distri-
bution map was obtained for each time point. Subsequently, the source map was
transformed into frequency domain and the ASSRs were calculated for each dipole
in order to develop the ASSR map.
Conclusions: Results demonstrate that the proposed MNI approach is successful
in reconstructing sources located both within (primary) and outside (non-primary)
of the AC. The non-primary sources are consistent with, and extends the literature.
The primary sources are detected in every experimental conditions indicating the
robustness of the approach. Furthermore, we demonstrate that the MNI approach is
capable of reconstructing the subcortical activities of ASSRs. Lastly, the results in-
dicate that the MNI approach outperform the previously used method of group-ICA.
Acknowledgements: Our special thanks go to Dr. Tine Goossens for sharing the
data used in this work. This work was supported by the Research Council, KU Leu-
ven through project OT/12/98 and by the Research Foundation Flanders through
FWO-project ZKC9024 and FWO-project ZKC5655.
AESoP symposium, Leuven, 16–18 September 2019 29Wednesday, September 18: Special Populations and Applications 2 11:10–12:30 Moderator: Andrev Dimitrijevic Neural speech tracking in the delta and theta frequency bands differentially encodes comprehension and intelligibil- ity of speech in noise Octave Etard (1), Tobias Reichenbach (1) (1) Imperial College London, Department of Bioengineering Objectives: Speech processing may be aided by cortical activity in the delta and theta frequency bands that tracks the speech envelope. Change of neural responses with the clarity and comprehension of speech have previously been established, but it remains unclear which aspects of neural speech tracking represent the processing of acoustic features, related to speech clarity, and which aspects reflect higher-level linguistic processing related to speech comprehension. Methods: We employed EEG to record neural responses of native English speakers listening to continuous speech in varying conditions of noise. We obtained EEG recordings in response to English stimuli as well as in response to a foreign unknown language (Dutch). When listening to English the subjects’ comprehension was mod- ulated by the noise level, but remained nil in the matched Dutch conditions. This allowed us to separate neural correlates of changes in the acoustic properties of the stimuli (clarity) and of speech comprehension. We used regularised linear spatio- temporal models to relate clarity and comprehension to the neural responses. Conclusion: We were able to predict speech comprehension and clarity in the dif- ferent acoustic conditions based on envelope tracking. We investigated the relative importance of the delta, theta and alpha frequency bands. We found that cortical tracking in the theta frequency band is mainly correlated to clarity, while the delta band contributed most to speech comprehension. 30 AESoP symposium, Leuven, 16–18 September 2019
Transcranial alternating current stimulation: mechanisms
and future directions
Myles Mc Laughlin (1), Boateng Asamoah (2), Ahmad Khatoun (3)
(1) Research Group ExpORL, Department of Neurosciences, KU Leuven
Transcranial alternating current stimulation (tACS) is a noninvasive neuromodu-
lation method which has been shown to modulate hearing, motor, cognitive and
memory function. However, the mechanisms underpinning these findings are con-
troversial, as studies show that the current reaching the cortex may not be strong
enough to entrain neural activity.
In this talk we present data that highlights a previously overlooked mechanism
through which tACS may work, and based on this propose a new minimally invasive
tACS approach.
Firstly, we suggest that some tACS effects are actually caused by transcutaneous
stimulation of peripheral nerves in the skin and not transcranial stimulation of cor-
tical neurons. Rhythmic activity from peripheral nerves then entrains cortical neu-
rons. We present data from a series of experiments in rats and humans that isolated
the transcranial and transcutaneous mechanisms and showed that the reported ef-
fects of tACS on the motor system can be caused by transcutaneous stimulation of
peripheral nerves.
Secondly, we propose that some of these issues can be overcome with a novel mini-
mally invasive approach where an electrode is implanted under the skin, directly on
the skull. We present data from animal and computational modeling studies that
support the feasibility of this novel approach.
AESoP symposium, Leuven, 16–18 September 2019 31Optimal parameters for obtaining robust parallel auditory brainstem responses at different intensities Melissa J. Polonenko (1), Ross K. Maddox (1,2) (1) University of Rochester Medical Center, Department of Neuroscience; (2) University of Rochester, Department of Biomedical Engineering Objectives: To evaluate 1) how presentation rate and level interact to affect auditory brainstem responses collected in parallel (pABR); and 2) the optimal parameters to generate pABR responses with maximal signal-to-noise ratios (SNR) in minimal recording times. Methods: Two-channel pABRs for 0.5, 1, 2, 4 and 8 kHz tonebursts were mea- sured for both ears of 20 adults with normal hearing. Twelve interleaved conditions were recorded in 2 hours and comprised 6 rates from 20–120 stimuli/s at 75 and 45 dBpeSPL. Responses were analyzed by cross-correlating the rectified impulse trains with the raw EEG and then averaged by weighting each epoch by the inverse variance of its pre-stimulus baseline. Results: Robust canonical responses were present and at both intensities and showed adaptation at higher rates. Latencies and amplitudes of wave V characteristically lengthened and decreased with a decrease in level respectively. A 40 Hz rate was optimal for yielding 0 dB SNR responses within
Poster Abstracts P1. On miniaturization effects and optimal channel selection for auditory attention decoding with EEG sensor networks Abhijith Mundanad Narayanan (1), Panagiotis Patrinos (1), Alexander Bertrand (1) (1) ESAT-STADIUS, Department of Electrical Engineering, KU Leuven Chronic EEG monitoring in daily life will require highly miniaturized EEG sensors, which by de- sign can only record within a small scalp area to avoid wires running over large distances across the head. To increase the spatial coverage, we envision a modular platform where a multitude of such wireless mini-EEG sensors are deployed, forming a so-called “wireless EEG sensor network” or WESN. We explore methods to identify the optimal scalp locations to position these sensors and analyze the performance loss due to miniaturization effects in an auditory attention decoding (AAD) task. To this end, candidate WESN nodes were created by pairing each electrode of a standard 64-channel EEG cap with its neighbors at
P3. Wave V eABR amplitude and latency in response to multi-pulse train stimulation Ludwig Englert (1), Ali Saeedi (1), Werner Hemmert (1) (1) Technical University of Munich, Department of Electrical Engineering and Computer Engineer- ing, Bio-Inspired Information Processing The electrically-evoked auditory brainstem responses (eABR) are signals which can be excited in cochlear impant (CI) users and are considered as helpful in diagnostic measures. In contrast to the typically used single pulses for eABR measurements in this study multi-pulse trains are used due to its similarity to the stimulus pattern used in clinics, whereby its still possible to evaluate the eABR signal. In this work thresholds (THRs) and mostcomfortable Levels (MCLs) were obtained for each sub- ject and multi-pulse condition. Datapoints were defined on DR, which is the difference of MCL and THR for each multi-pulse condition. For each datapoint the eABR signal was measured and evalu- ated. The dataset for each multi-pulse condition was used to analyse the latency-intensity function and the amplitude-growth function. The latency-intensity function shows a decreasing pattern for higher stimulation amplitudes across all multi-pulse conditions. The slope of the amplitude-growth function decreases for higher number of pulses. The multi-pulses as used in this work are delivered in a time span in order of the refractori- ness period of a single neuron. Therefore these results provide more information about the electrical excitation of auditory nerve fibers and the progression of the signal through the auditory brainstem. Furthermore the data is used in a subsequent study to estimate clinical THRs. P4. Estimation of clinical thresholds by means of eABR thresholds in response to multi-pulse trains in cochlear im- plant users Ali Saeedi (1), Ludwig Englert (1), Werner Hemmert (1) (1) Technical University of Munich, Department of Electrical Engineering and Computer Engineer- ing, Bio-Inspired Information Processing It is known that behavioural thresholds (THR) depend not only on the stimulation amplitude but also on stimulation rate. When stimulating with single pulses, eABR THRs show strong correlation with behavioural THRs. However, for high-rate stimulation, such as used for clinical purposes, the correlation is weaker. In this study, we employed multi-pulse trains as stimuli to see if they can estimate clinical THRs. Multi-pulse trains of 1-, 2-, 4-, 8-, and 16-pulses with a repetition rate of 37 Hz were delivered to a medial electrode of six CI users with MEDEL implants. Clinical THRs and MCLs were also measure in response to stimuli of rate of 1000 pps. EABR amplitude growth functions (AGF) were then measured starting from a level of 95 % of the DR down to a point where eABR waves amplitudes were just above the noise floor. EABR THR was defined where the extrapolated fitted curve was zero. Over all subjects, the median correlation between estimated eABR THR and psychophysical THR was 0.79 and 0.78 for waves III and V, respectively. Generally, the estimated eABR THR decreased with increasing number of pulses. The estimated eABR THRs for 1- and 2-pulse conditions were still well-above clinical THRs (median difference = 147 and 101 µA, respectively for wave V). For the 4-, 8-, and 16-pulse conditions, the estimated eABR THRs were much closer to the clinical THR (median difference = −22, 4, and 12 µA, respectively for wave V). 34 AESoP symposium, Leuven, 16–18 September 2019
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