Towards auditory attention decoding with noise-tagging: A pilot study
This is an incremental pilot study for neuro-steered hearing devices and brain-computer interfaces, focusing on enhancing speaker detection in multi-speaker environments.
The study tackled auditory attention decoding (AAD) by testing noise-tagging in speech stimuli, finding that conventional AAD methods with 70-100% modulation depths outperformed unmodulated audio, but noise-code decoding did not improve results.
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.