English

Towards auditory attention decoding with noise-tagging: A pilot study

Neurons and Cognition 2024-10-15 v2 Artificial Intelligence Machine Learning Sound Audio and Speech Processing

Abstract

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.

Keywords

Cite

@article{arxiv.2403.15523,
  title  = {Towards auditory attention decoding with noise-tagging: A pilot study},
  author = {H. A. Scheppink and S. Ahmadi and P. Desain and M. Tangermann and J. Thielen},
  journal= {arXiv preprint arXiv:2403.15523},
  year   = {2024}
}

Comments

6 pages, 2 figures, 9th Graz Brain-Computer Interface Conference 2024

R2 v1 2026-06-28T15:30:32.154Z