Selective auditory attention the brain's ability to focus on a specific speaker in multi-talker environments is often compromised in individuals with auditory or neurological disorders. While Auditory Attention Decoding (AAD) using EEG has shown promise in detecting attentional focus, existing models primarily utilize temporal or spectral features, often neglecting the synergistic relationships across time, space, and frequency. This limitation significantly reduces decoding accuracy, particularly in short decision windows, which are crucial for real-time applications like neuro-steered hearing aids. This study is to enhance short-window AAD performance by fully leveraging multi-dimensional EEG characteristics.

To address this, we propose TSF-AADNet, a novel neural framework that integrates temporal–spatial and frequency–spatial features using dual-branch architectures and advanced attention-based fusion.

Tested on KULeuven and DTU datasets, TSF-AADNet achieves 91.8% and 81.1% accuracy at 0.1-second windows—outperforming the state-of-the-art by up to 7.99%.

These results demonstrate the model's potential in enabling precise, real-time attention tracking for hearing impairment diagnostics and next-generation neuroadaptive auditory prosthetics.