Deep learning-based auditory attention decoding in listeners with hearing impairment

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Tanveer, M AsjidLund University,Lund Univ, Sweden (författare)

Deep learning-based auditory attention decoding in listeners with hearing impairment

Artikel/kapitelEngelska2024

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IOP Publishing Ltd,2024

Nummerbeteckningar

LIBRIS-ID:oai:lup.lub.lu.se:cdcf3b1a-ff48-4ae9-b38d-f5dac7815f97
https://lup.lub.lu.se/record/cdcf3b1a-ff48-4ae9-b38d-f5dac7815f97URI
https://doi.org/10.1088/1741-2552/ad49d7DOI
https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-204300URI

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Språk:engelska
Sammanfattning på:engelska

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Funding Agencies|ELLIIT
This study develops a deep learning method for fast auditory attention decoding (AAD) using electroencephalography (EEG) from listeners with hearing impairment. It addresses three classification tasks: differentiating noise from speech-in-noise, classifying the direction of attended speech (left vs. right) and identifying the activation status of hearing aid noise reduction (NR) algorithms (OFF vs. ON). These tasks contribute to our understanding of how hearing technology influences auditory processing in the hearing-impaired population. Method: Deep convolutional neural network (DCNN) models were designed for each task. Two training strategies were employed to clarify the impact of data splitting on AAD tasks: inter-trial, where the testing set used classification windows from trials that the training set hadn't seen, and intra-trial, where the testing set used unseen classification windows from trials where other segments were seen during training. The models were evaluated on EEG data from 31 participants with hearing impairment, listening to competing talkers amidst background noise. Results: Using 1-second classification windows, DCNN models achieve accuracy (ACC) of 69.8\%, 73.3\% and 82.9\% and area-under-curve (AUC) of 77.2\%, 80.6\% and 92.1\% for the three tasks respectively on inter-trial strategy. In the intra-trial strategy, they achieved ACC of 87.9\%, 80.1\% and 97.5\%, along with AUC of 94.6\%, 89.1\%, and 99.8\%. Our DCNN models show good performance on short 1-second EEG samples, making them suitable for real-world applications. Conclusion: Our DCNN models successfully addressed three tasks with short 1-second EEG windows from participants with hearing impairment, showcasing their potential. While the inter-trial strategy demonstrated promise for assessing AAD, the intra-trial approach yielded inflated results, underscoring the important role of proper data splitting in EEG-based AAD tasks. Significance: Our findings showcase the promising potential of EEG-based tools for assessing auditory attention in clinical contexts and advancing hearing technology, while also promoting further exploration of alternative deep learning architectures and their potential constraints.

Ämnesord och genrebeteckningar

TEKNIK OCH TEKNOLOGIER Elektroteknik och elektronik Reglerteknik hsv//swe
ENGINEERING AND TECHNOLOGY Electrical Engineering, Electronic Engineering, Information Engineering Control Engineering hsv//eng
TEKNIK OCH TEKNOLOGIER Medicinteknik Annan medicinteknik hsv//swe
ENGINEERING AND TECHNOLOGY Medical Engineering Other Medical Engineering hsv//eng
EEG analysis
hearing aids
auditory attention decoding; deep convolutional neural network; deep learning; EEG; hearing impairment; inter/intra trial

Biuppslag (personer, institutioner, konferenser, titlar ...)

Skoglund, MartinLinköpings universitet,Reglerteknik,Tekniska fakulteten,Eriksholm Res Ctr, Denmark(Swepub:liu)marsk27 (författare)
Bernhardsson, BoLund University,Lunds universitet,Institutionen för reglerteknik,Institutioner vid LTH,Lunds Tekniska Högskola,Department of Automatic Control,Departments at LTH,Faculty of Engineering, LTH,Lund Univ, Sweden(Swepub:lu)cont-bbe (författare)
Alickovic, EminaLinköpings universitet,Reglerteknik,Tekniska fakulteten,Eriksholm Res Ctr, Denmark(Swepub:liu)emial07 (författare)
Lund UniversityLund Univ, Sweden (creator_code:org_t)

Sammanhörande titlar

Ingår i:Journal of Neural Engineering: IOP Publishing Ltd21:31741-25601741-2552

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Av författaren/redakt...: Tanveer, M Asjid; Skoglund, Martin; Bernhardsson, Bo; Alickovic, Emina

Om ämnet

TEKNIK OCH TEKNOLOGIER: TEKNIK OCH TEKNO ...; och Elektroteknik oc ...; och Reglerteknik

TEKNIK OCH TEKNOLOGIER: TEKNIK OCH TEKNO ...; och Medicinteknik; och Annan medicintek ...

Artiklar i publikationen: Journal of Neura ...

Av lärosätet: Lunds universitet; Linköpings universitet

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