Towards auditory attention decoding with noise-tagging: A pilot study
<|reference_start|>Towards auditory attention decoding with noise-tagging: A pilot study: Auditory attention decoding (AAD) aims to extract from brain activity the attended speaker amidst candidate speakers, offering promising applications for neuro-steered hearing devices and brain-computer interfacing. This pilot study makes a first step towards AAD using the noise-tagging stimulus protocol, which evokes reliable code-modulated evoked potentials, but is minimally explored in the auditory modality. Participants were sequentially presented with two Dutch speech stimuli that were amplitude-modulated with a unique binary pseudo-random noise-code, effectively tagging these with additional decodable information. We compared the decoding of unmodulated audio against audio modulated with various modulation depths, and a conventional AAD method against a standard method to decode noise-codes. Our pilot study revealed higher performances for the conventional method with 70 to 100 percent modulation depths compared to unmodulated audio. The noise-code decoder did not further improve these results. These fundamental insights highlight the potential of integrating noise-codes in speech to enhance auditory speaker detection when multiple speakers are presented simultaneously.<|reference_end|>
@article{scheppink2024towards,
    title={Towards auditory attention decoding with noise-tagging: A pilot study},
    author={H. A. Scheppink, S. Ahmadi, P. Desain, M. Tangermann, J. Thielen},
    journal={9th Graz Brain-Computer Interface Conference (2024) 337-342},
    year={2024},
    doi={10.3217/978-3-99161-014-4-059},
    archivePrefix={arXiv},
    eprint={2403.15523},
    primaryClass={q-bio.NC cs.AI cs.LG cs.SD eess.AS}
}