Improving EEG-based decoding of the locus of auditory attention through domain adaptation

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Wilroth, JohannaLinköping University,Linköpings universitet,Reglerteknik,Tekniska fakulteten,Linkoping University (author)

Improving EEG-based decoding of the locus of auditory attention through domain adaptation

Article/chapterEnglish2023

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Institute of Physics (IOP),2023
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LIBRIS-ID:oai:DiVA.org:bth-25836
https://urn.kb.se/resolve?urn=urn:nbn:se:bth-25836URI
https://doi.org/10.1088/1741-2552/ad0e7bDOI
https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-200034URI
https://lup.lub.lu.se/record/6f634fa5-9806-44e4-bf9c-4fa40d32b39aURI

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Language:English
Summary in:English

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Subject category:art swepub-publicationtype

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Funding Agencies|ELLIIT Strategic Research Area
Objective. This paper presents a novel domain adaptation (DA) framework to enhance the accuracy of electroencephalography (EEG)-based auditory attention classification, specifically for classifying the direction (left or right) of attended speech. The framework aims to improve the performances for subjects with initially low classification accuracy, overcoming challenges posed by instrumental and human factors. Limited dataset size, variations in EEG data quality due to factors such as noise, electrode misplacement or subjects, and the need for generalization across different trials, conditions and subjects necessitate the use of DA methods. By leveraging DA methods, the framework can learn from one EEG dataset and adapt to another, potentially resulting in more reliable and robust classification models. Approach. This paper focuses on investigating a DA method, based on parallel transport, for addressing the auditory attention classification problem. The EEG data utilized in this study originates from an experiment where subjects were instructed to selectively attend to one of the two spatially separated voices presented simultaneously. Main results. Significant improvement in classification accuracy was observed when poor data from one subject was transported to the domain of good data from different subjects, as compared to the baseline. The mean classification accuracy for subjects with poor data increased from 45.84% to 67.92%. Specifically, the highest achieved classification accuracy from one subject reached 83.33%, a substantial increase from the baseline accuracy of 43.33%. Significance. The findings of our study demonstrate the improved classification performances achieved through the implementation of DA methods. This brings us a step closer to leveraging EEG in neuro-steered hearing devices. © 2023 The Author(s). Published by IOP Publishing Ltd.

Subject headings and genre

TEKNIK OCH TEKNOLOGIER Elektroteknik och elektronik Signalbehandling hsv//swe
ENGINEERING AND TECHNOLOGY Electrical Engineering, Electronic Engineering, Information Engineering Signal Processing hsv//eng
MEDICIN OCH HÄLSOVETENSKAP Medicinsk bioteknologi Biomedicinsk laboratorievetenskap/teknologi hsv//swe
MEDICAL AND HEALTH SCIENCES Medical Biotechnology Biomedical Laboratory Science/Technology hsv//eng
TEKNIK OCH TEKNOLOGIER Elektroteknik och elektronik Reglerteknik hsv//swe
ENGINEERING AND TECHNOLOGY Electrical Engineering, Electronic Engineering, Information Engineering Control Engineering hsv//eng
auditory attention classification
domain adaptation
EEG
locus of attention
parallel transport
transfer learning
Audition
Classification (of information)
Electrophysiology
Adaptation framework
Adaptation methods
Auditory attention
Classification accuracy
Locus of attentions
Novel domain
Electroencephalography

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Bernhardsson, BoLund University,Lunds universitet,Institutionen för reglerteknik,Institutioner vid LTH,Lunds Tekniska Högskola,LTH profilområde: AI och digitalisering,LTH profilområden,LTH profilområde: Teknik för hälsa,LU profilområde: Naturlig och artificiell kognition,Lunds universitets profilområden,Department of Automatic Control,Departments at LTH,Faculty of Engineering, LTH,LTH Profile Area: AI and Digitalization,LTH Profile areas,Faculty of Engineering, LTH,LTH Profile Area: Engineering Health,Faculty of Engineering, LTH,LU Profile Area: Natural and Artificial Cognition,Lund University Profile areas(Swepub:lu)cont-bbe (author)
Heskebeck, FridaLund University,Lunds universitet,Institutionen för reglerteknik,Institutioner vid LTH,Lunds Tekniska Högskola,LTH profilområde: AI och digitalisering,LTH profilområden,Department of Automatic Control,Departments at LTH,Faculty of Engineering, LTH,LTH Profile Area: AI and Digitalization,LTH Profile areas,Faculty of Engineering, LTH(Swepub:lu)fr2166he (author)
Skoglund, MartinLinköpings universitet,Reglerteknik,Tekniska fakulteten,Oticon AS, Denmark,Linkoping University(Swepub:liu)marsk27 (author)
Bergeling, Carolina,1990-Blekinge Institute of Technology,Blekinge Tekniska Högskola,Institutionen för matematik och naturvetenskap,Blekinge Inst Technol, Sweden(Swepub:bth)cbg (author)
Alickovic, EminaLinköping University,Linköpings universitet,Reglerteknik,Tekniska fakulteten,Oticon AS, Denmark,Linkoping University(Swepub:liu)emial07 (author)
Linköpings universitetReglerteknik (creator_code:org_t)

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In:Journal of Neural Engineering: Institute of Physics (IOP)20:61741-25601741-2552

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