We introduce a novel auditory brain-computer interface (BCI) paradigm, Auditory Intention Decoding (AID), designed to enhance communication capabilities within the brain-AI interface (BAI) system EEGChat. AID enables users to select among multiple auditory options (intentions) by analyzing their brain responses, offering a pathway to construct a communication system that requires neither muscle movement nor syntactic formation. To evaluate the feasibility of this paradigm, we conducted a proof-of-concept study. The results demonstrated statistically significant decoding performance, validating the approach's potential. Despite these promising findings, further optimization is required to enhance system performance and realize the paradigm's practical application.
@article{arxiv.2504.13937,
title = {Auditory Conversational BAI: A Feasibility Study},
author = {Michal Robert Žák and Moritz Grosse-Wentrup},
journal= {arXiv preprint arXiv:2504.13937},
year = {2025}
}
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Created for the NAT conference 2025 (https://neuroadaptive.org/conference/2025/)