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The paper introduces NEUROTOKEN, a unified neural network for auditory attention decoding (AAD) that jointly predicts the attended speaker’s direction and source by modeling the conditional likelihood of the attended envelope given EEG. It employs a conditional flow‑matching head (ATTUNEFLOW) and two inference‑time ensembles (QUADTRACK and ENV‑FLOW) to improve source‑AAD accuracy and reduce variance across subjects. Experiments on KU Leuven, DTU, and NJU datasets show significant gains over existing baselines and reveal that prior direction‑AAD results overestimate performance under stricter protocols.
arXiv:2610.15339v1 Announce Type: new Abstract: Current fMRI encoding models frame prediction as deterministic regression, yet identical stimuli evoke a distribution of neural responses (arising from...
arXiv:2605. 00865v2 Announce Type: replace-cross Abstract: We tested whether auditory-evoked EEG supports subject-independent five-vowel perception decoding when trial identity, model identity, prediction provenance, and participant-level inference are controlled within a single benchmark.
The study evaluated whether imagined speech decoded from scalp EEG can transfer across individuals using three public datasets. Across 482 participant pairs, cross‑subject decoding accuracy was essentially at chance, while within‑subject decoding only surpassed chance in one dataset. The results suggest that imagined‑speech content does not generalize between people and that larger single‑condition datasets are more beneficial than further model development.
arXiv:2606. 24087v1 Announce Type: new Abstract: Reconstructing continuous speech from scalp electroencephalography (EEG) remains fundamentally challenging.
arXiv:2610.14507v1 Announce Type: new Abstract: Generative modeling offers a promising solution to the scarcity of labeled clinical electroencephalography (EEG) data under strict privacy constraints....