19 citations · 28 across the 7 of their papers we have counts for
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cs.SD2026
Addressing Limited Data in Auditory Attention Decoding with Diffusion Generative Models
David Rannaleet, Victor Gunnarsson, Bo Bernhardsson +2
Limited training data constrains deep learning models for Auditory Attention Decoding (AAD) in hearing aids (HAs). AAD uses electroencephalogram (EEG) data to decode listener's att…
eess.SP2026
Neural Tracking of Sustained Attention, Attention Switching, and Natural Conversation in Audiovisual Environments using Mobile EEG
Johanna Wilroth, Oskar Keding, Martin A. Skoglund +3
Everyday communication is dynamic and multisensory, often involving shifting attention, overlapping speech and visual cues. Yet, most neural attention tracking studies are still li…