Unsupervised Self-Adaptive Auditory Attention Decoding

When multiple speakers talk simultaneously, a hearing device cannot identify which of these speakers the listener intends to attend to. Auditory attention decoding (AAD) algorithms can provide this information by, for example, reconstructing the attended speech envelope from electroencephalography (...

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Vydané v:IEEE journal of biomedical and health informatics Ročník 25; číslo 10; s. 3955 - 3966
Hlavní autori: Geirnaert, Simon, Francart, Tom, Bertrand, Alexander
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.10.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Attention
auditory attention decoding
Auditory system
Correlation
Decoders
Decoding
EEG
Electroencephalography
Hearing
Hearing aids
Heuristic algorithms
Iterative methods
Mathematical analysis
neuro-steered hearing devices
Performance evaluation
stimulus reconstruction
Training
unsupervised training
User training
ISSN:2168-2194, 2168-2208, 2168-2208
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Shrnutí:When multiple speakers talk simultaneously, a hearing device cannot identify which of these speakers the listener intends to attend to. Auditory attention decoding (AAD) algorithms can provide this information by, for example, reconstructing the attended speech envelope from electroencephalography (EEG) signals. However, these stimulus reconstruction decoders are traditionally trained in a supervised manner, requiring a dedicated training stage during which the attended speaker is known. Pre-trained subject-independent decoders alleviate the need of having such a per-user training stage but perform substantially worse than supervised subject-specific decoders that are tailored to the user. This motivates the development of a new unsupervised self-adapting training/updating procedure for a subject-specific decoder, which iteratively improves itself on unlabeled EEG data using its own predicted labels. This iterative updating procedure enables a self-leveraging effect, of which we provide a mathematical analysis that reveals the underlying mechanics. The proposed unsupervised algorithm, starting from a random decoder, results in a decoder that outperforms a supervised subject-independent decoder. Starting from a subject-independent decoder, the unsupervised algorithm even closely approximates the performance of a supervised subject-specific decoder. The developed unsupervised AAD algorithm thus combines the two advantages of a supervised subject-specific and subject-independent decoder: it approximates the performance of the former while retaining the 'plug-and-play' character of the latter. As the proposed algorithm can be used to automatically adapt to new users, as well as over time when new EEG data is being recorded, it contributes to more practical neuro-steered hearing devices.
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ISSN:2168-2194
2168-2208
2168-2208
DOI:10.1109/JBHI.2021.3075631