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Adaptive attention-driven speech enhancement for EEG-informed hearing prostheses.

Neetha Das, Simon Van Eyndhoven, Tom Francart

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 9, 2017
    PubMed
    Summary

    This study introduces a neuro-steered hearing aid that uses electroencephalography (EEG) to detect auditory attention. This technology improves noise reduction in complex environments by identifying the target speaker for enhanced speech intelligibility.

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    Area of Science:

    • Neuroscience
    • Signal Processing
    • Audiology

    Background:

    • Hearing prostheses use noise reduction to enhance speech intelligibility.
    • Acoustic noise reduction is challenging in multi-speaker environments due to difficulties in identifying target speakers.
    • Electroencephalography (EEG) enables auditory attention detection (AAD) by analyzing neural activity.

    Purpose of the Study:

    • To combine EEG-based AAD with a multi-channel Wiener filter (MWF) for improved noise reduction.
    • To analyze the impact of AAD accuracy on the performance of an adaptive MWF.
    • To evaluate a neuro-steered MWF system in scenarios with shifting auditory attention.

    Main Methods:

    • Implementing an EEG-based auditory attention detection (AAD) system.
    • Integrating AAD with a multi-channel Wiener filter (MWF) to create a neuro-steered MWF.
    • Utilizing a sliding-window approach for an adaptive MWF, simulating attention shifts between two speakers.

    Main Results:

    • The study demonstrates the feasibility of a neuro-steered MWF system.
    • Analysis quantifies the relationship between AAD accuracy and noise suppression performance.
    • The system's effectiveness is evaluated under dynamic listening conditions.

    Conclusions:

    • EEG-based AAD can effectively guide acoustic noise reduction algorithms.
    • Neuro-steered MWF shows promise for enhancing speech intelligibility in complex auditory scenes.
    • This approach offers a potential advancement for hearing prosthesis technology.