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: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.
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: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: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: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.
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: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.
This study evaluates imagined speech decoding from EEG using a strictly subject‑independent framework. Two pipelines—time‑domain statistical features and frequency‑domain spectral bandpower—were compared with a random forest classifier; the spectral approach achieved higher trial‑wise accuracy (49.03 % vs. 37.97 %). Feature selection revealed that only a few frequency bands carried most discriminative information, providing a solid baseline for future cross‑subject BCI research.
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.