arXiv:2606. 24164v1 Announce Type: cross Abstract: Recent end-to-end models for EEG-guided target speech extraction report impressive results, underscoring potential for neuro-steered hearing technologies.
Recent end-to-end models for EEG-guided target speech extraction report impressive results, underscoring potential for neuro-steered hearing technologies. However, our analysis reveals that high within-trial performance can be driven by trial-specific EEG structure that acts as shortcuts for target selection, leading to poor generalization on unseen trials.
arXiv:2607. 18345v1 Announce Type: cross Abstract: Limited training data constrains deep learning models for Auditory Attention Decoding (AAD) in hearing aids (HAs).
arXiv:2501. 09700v2 Announce Type: replace-cross Abstract: Electroencephalogram (EEG) signals have emerged as a promising modality for biometric identification.
arXiv:2606. 24087v1 Announce Type: new Abstract: Reconstructing continuous speech from scalp electroencephalography (EEG) remains fundamentally challenging.
arXiv:2601. 17883v3 Announce Type: replace Abstract: Electroencephalography (EEG) foundation models (FMs) have recently emerged as a promising paradigm for brain-computer interfaces, aiming to learn transferable neural representations from large-scale heterogeneous recordings.
arXiv:2607. 25626v1 Announce Type: new Abstract: Decoding speech information directly from scalp electroencephalography (EEG) into text provides a potential non-invasive neural communication pathway for individuals with severe speech and motor impairments.
arXiv:2508. 17742v3 Announce Type: replace-cross Abstract: Electroencephalography foundation models (EEG-FMs) have advanced brain signal analysis, but the lack of standardized evaluation benchmarks impedes model comparison and scientific progress.
arXiv:2608. 13072v1 Announce Type: new Abstract: Electroencephalography (EEG) decoding models often generalize poorly across datasets and subjects due to domain shifts in acquisition protocols and individual neurophysiology.
arXiv:2607. 05165v1 Announce Type: new Abstract: Non-invasive brain-to-speech decoding aims to restore communication to patients suffering from neurodegenerative disease, without the risks of neurosurgery.
SHINE is a Sequential Hierarchical Integration Network designed to reconstruct speech envelope and Mel spectrogram from EEG and MEG recordings. It uses a residual sensor adapter, dilated-block states for temporal depth, and a target- and time-dependent gate to fuse hierarchical and attention-enhanced context predictions. Across two EEG and two MEG datasets, SHINE achieved the highest mean envelope and mean-Mel Pearson correlations among nine baseline methods and ranked second in the NeurIPS 2025 PNPL Competition’s speech-detection Extended Track.
arXiv:2604. 16926v2 Announce Type: replace-cross Abstract: Electroencephalography (EEG) foundation models have shown strong potential for learning generalizable representations from large-scale neural data, yet their clinical deployment is hindered by distribution shifts across clinical settings, devices, and populations.
EEG-VID is a task‑guided latent predictive pretraining framework designed to improve EEG decoding across session and subject shifts. It predicts future latent EEG states from recent history using an exponential‑moving‑average target encoder and weak task guidance, then fine‑tunes with supervised learning. The method achieves significant accuracy gains on VIG‑48 and BCI Competition datasets, and demonstrates effective assistive target selection in a robot‑scene study.