liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Direct Estimation of Linear Filters for EEG Source-Localization in a Competing-Talker Scenario
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0003-3599-9160
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering. Eriksholm Research Centre, Oticon A/S, Snekkersten, Denmark.ORCID iD: 0000-0001-9183-3427
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering. Eriksholm Research Centre, Oticon A/S, Snekkersten, Denmark.ORCID iD: 0000-0002-4655-9112
2023 (English)In: Special issue: 22nd IFAC World Congress / [ed] Hideaki Ishii, Yoshio Ebihara, Jun-ichi Imura, Masaki Yamakita, ELSEVIER , 2023, Vol. 56, no 2, p. 6510-6517Conference paper, Published paper (Refereed)
Abstract [en]

Hearing-impaired listeners have a reduced ability to selectively attend to sounds of interest amid distracting sounds in everyday environments. This ability is not fully regained with modern hearing technology. A better understanding of the brain mechanisms underlying selective attention during speech processing may lead to brain-controlled hearing aids with improved detection and amplification of the attended speech. Prior work has shown that brain responses to speech, measured with magnetoencephalography (MEG) or electroencephalography (EEG), are modulated by selective attention. These responses can be predicted from the speech signal through linear filters called Temporal Response Functions (TRFs). Unfortunately, these sensor-level predictions are often noisy and do not provide much insight into specific brain source locations. Therefore, a novel method called Neuro-Current Response Functions (NCRFs) was recently introduced to directly estimate linear filters at the brain source level from MEG responses to speech from one talker. However, MEG is not well-suited for wearable and realtime hearing technologies. This work aims to adapt the NCRF method for EEG under more realistic listening environments. EEG data was recorded from a hearing-impaired listener while attending to one of two competing talkers embedded in 16-talker babble noise. Preliminary results indicate that source-localized linear filters can be directly estimated from EEG data in such competing-talker scenarios. Future work will focus on evaluating the current method on a larger dataset and on developing novel methods, which may aid in the improvement of next-generation brain-controlled hearing technology.

Place, publisher, year, edition, pages
ELSEVIER , 2023. Vol. 56, no 2, p. 6510-6517
Series
IFAC PAPERSONLINE, E-ISSN 2405-8963
Keywords [en]
Bio-signals analysis and interpretation, Brain-machine interaction, Time series modelling, Linear systems, Time-delay systems, Biomedical and medical image processing and systems, Cognitive systems engineering, Modeling of human performance, Physiological Model
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-202437DOI: 10.1016/j.ifacol.2023.10.298OAI: oai:DiVA.org:liu-202437DiVA, id: diva2:1850897
Conference
22nd World Congress of the International Federation of Automatic Control (IFAC), Yokohama, JAPAN, jul 09-14, 2023
Available from: 2024-04-11 Created: 2024-04-11 Last updated: 2024-04-16
In thesis
1. Exploring Auditory Attention Using EEG
Open this publication in new window or tab >>Exploring Auditory Attention Using EEG
2024 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Listeners with normal-hearing often overlook their ability to comprehend speech in noisy environments effortlessly. Our brain’s adeptness at identifying and amplifying attended voices while suppressing unwanted background noise, known as the cocktail party problem, has been extensively researched for decades. Yet, many aspects of this complex puzzle remain unsolved and listeners with hearing-impairment still struggle to focus on a specific speaker in noisy environments. While recent intelligent hearing aids have improved noise suppression, the problem of deciding which speaker to enhance remains unsolved, leading to discomfort for many hearing aid users in noisy environments.

In this thesis, we explore the complexities of the human brain in challenging auditory environments. Two datasets are investigated where participants were tasked to selectively attend to one of two competing voices, replicating a cocktail-party scenario. The auditory stimuli trigger neurons to generate electrical signals that propagate in all directions. When a substantial number of neurons fire simultaneously, their collective electrical signal becomes detectable by small electrodes placed on the head. This method of measuring brain activity, known as electroencephalography (EEG), holds potential to provide feedback to the hearing aids, enabling adjustments to enhance attended voice(s).

EEG data is often noisy, incorporating neural responses with artifacts such as muscle movements, eye blinks and heartbeats. In the first contribution of this thesis, we focus on comparing different manual and automatic artifact-rejection techniques and assessing their impact on auditory attention decoding (AAD).

While EEG measurements offer high temporal accuracy, spatial resolution is inferior compared to alternative tools like magnetoencephalography (MEG). This difference poses a considerable challenge for source localization with EEG data. In the second contribution of this thesis, we demonstrate anticipated activity in the auditory cortex using EEG data from a single listener, employing Neuro-Current Response Functions (NCRFs). This method, previously evaluated only with MEG data, holds significant promise in hearing aid development.

EEG data may involve both linear and nonlinear components due to the propagation of the electrical signals through brain tissue, skull, and scalp with varying conductivities. In the third contribution, we aim to enhance source localization by introducing a binning-based nonlinear detection and compensation method. The results suggest that compensating for some nonlinear components produces more precise and synchronized source localization compared to original EEG data.

In the fourth contribution, we present a novel domain adaptation framework that improves AAD performances for listeners with initially low classification accuracy. This framework focuses on classifying the direction (left or right) of attended speech and shows a significant accuracy improvement when transporting poor data from one listener to the domain of good data from different listeners.

Taken together, the contributions of this thesis hold promise for improving the lives of hearing-impaired individuals by closing the loop between the brain and hearing aids.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2024. p. 42
Series
Linköping Studies in Science and Technology. Licentiate Thesis, ISSN 0280-7971 ; 1993
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-202438 (URN)10.3384/9789180756327 (DOI)9789180756310 (ISBN)9789180756327 (ISBN)
Presentation
2024-05-16, Ada Lovelace, B Building, Campus Valla, Linköping, 13:15 (English)
Opponent
Supervisors
Available from: 2024-04-15 Created: 2024-04-15 Last updated: 2024-04-15Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Authority records

Wilroth, JohannaKulasingham, Joshua P.Skoglund, Martin A.Alickovic, Emina

Search in DiVA

By author/editor
Wilroth, JohannaKulasingham, Joshua P.Skoglund, Martin A.Alickovic, Emina
By organisation
Automatic ControlFaculty of Science & Engineering
Signal Processing

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 25 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf