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Improving EEG-based decoding of the locus of auditory attention through domain adaptation
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- Wilroth, Johanna (author)
- Linköping University,Linköpings universitet,Reglerteknik,Tekniska fakulteten,Linkoping University
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- Bernhardsson, Bo (author)
- Lund 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
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- Heskebeck, Frida (author)
- Lund 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
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- (creator_code:org_t)
- Institute of Physics (IOP), 2023
- 2023
- English.
- In: Journal of Neural Engineering. - : Institute of Physics (IOP). - 1741-2560 .- 1741-2552. ; 20:6
- Journal article (peer-reviewed)
Abstract
Subject headings
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- 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
- 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)
Keyword
- 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
Publication and Content Type
- ref (subject category)
- art (subject category)
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