arXiv:2606. 24087v1 Announce Type: new Abstract: Reconstructing continuous speech from scalp electroencephalography (EEG) remains fundamentally challenging.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
This survey reviews recent advances in converting non‑invasive EEG signals into images, text, and audio using generative AI techniques such as GANs, VAEs, transformers, and diffusion models. It summarizes datasets, feature‑encoding methods, evaluation metrics, and key challenges, noting that EEG‑to‑image models mainly use encoder‑decoder architectures, EEG‑to‑text leverages transformer language models, and EEG‑to‑audio maps signals to mel‑spectrograms for vocoder synthesis. The paper highlights the limitations of small, heterogeneous datasets, poor cross‑subject generalization, and the lack of standardized benchmarks, while providing open‑source resources to support reproducible research.
arXiv:2511. 11686v4 Announce Type: replace Abstract: Speech enhancement (SE) requires high-fidelity reconstruction of clean speech that preserves linguistic and paralinguistic cues while maintaining high perceptual quality.
arXiv:2607. 18345v1 Announce Type: cross Abstract: Limited training data constrains deep learning models for Auditory Attention Decoding (AAD) in hearing aids (HAs).
arXiv:2608. 00048v1 Announce Type: cross Abstract: Electroencephalography (EEG) generation is essential for alleviating data scarcity and enabling large scale neural modeling in brain computer interface applications.
arXiv:2601. 09239v5 Announce Type: replace-cross Abstract: Speech tokenizers are a key building block of fully discrete Speech LLMs.
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.