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.
By Xiran Xu, Yujie Yan, Songyi Li, Linze Zheng, Zifeng Zhang, Mochu Dong, Jing Chen
arXiv:2606. 16327v1 Announce Type: cross Abstract: Recent acoustic-to-articulatory inversion (AAI) models rely on electromagnetic articulography (EMA) data, which are costly and limited in scale.
By Hyung Kyu Kim, Byungchan Hwang, Hak Gu Kim
NeuroCodec is a novel generative framework that uses EEG signals to guide target speaker extraction in multi‑talker environments. It operates in the latent space of a neural audio codec, modeling EEG spatial dependencies with graph convolutions and fusing EEG and speech representations via attention and Mamba‑based temporal modeling. A six‑phase training curriculum and a causal streaming version enable high‑fidelity, 44.1 kHz audio reconstruction up to 20 kHz, outperforming baselines on Cocktail Party and KUL datasets and receiving higher perceptual scores in a listening test.
By Julian Silva, Jaliya Kodithuwakku, Navindu Sirimanna, Shaveen Herath, Wageesha N. Manamperi, Joshua P. Kulasingham
arXiv:2610.09792v1 Announce Type: new
Abstract: Electroencephalography (EEG) is highly susceptible to electromyographic (EMG) artifacts, whose temporal heterogeneity and spatial-spectral overlap with...
By Lu Wang-N\"oth, Hai Huang, Philipp Heiler, Shuqiong Wu, Liyun Zhang, Helmut Mayer
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.
By Benjamin Ballyk, Teyun Kwon, Miran \"Ozdogan, Oiwi Parker Jones
This scoping review examines how brain‑computer interfaces (BCIs) can restore sensory and motor functions in people with severe neurological impairment. It introduces a unified 2×2 framework (invasiveness × signal direction) to map 31 key studies, highlighting that most evidence comes from invasive, efferent‑restoration approaches post‑2015. The paper outlines a roadmap toward closed‑loop, bidirectional restoration and identifies gaps in metric standardization, longitudinal data, and cross‑community collaboration.
By Xuan-The Tran
arXiv:2609.20904v1 Announce Type: cross
Abstract: Hybrid motor-imagery brain-computer interfaces (MI-BCIs) combining EEG and fNIRS can outperform EEG-only systems by exploiting complementary electrop...
By Boyuan Zhao, Sifan Zhang, Luping Chen
arXiv:2606. 01884v1 Announce Type: new Abstract: Practical non-invasive Brain-Computer Interface (BCI) systems require EEG decoders with strong cross-subject generalization and minimal calibration.
By Ziyuan Li, Yueyu Sun, Yimeng Zhang
arXiv:2609.36687v1 Announce Type: cross
Abstract: Neural codecs encode continuous signals into compact sequences of discrete tokens, providing an interface for efficient transmission, storage, and to...
By Jihwan Lee, Kleanthis Avramidis, Junhyeok Lee, Tiantian Feng, Najim Dehak, Shrikanth Narayanan
arXiv:2604. 01832v1 Announce Type: cross Abstract: We introduce GAP-URGENet, a generative-predictive fusion framework developed for Track 1 of the ICASSP 2026 URGENT Challenge.
By Xiaobin Rong, Yushi Wang, Zheng Wang, Jing Lu
The paper introduces the Subject-Invariant Cross-Modal Perceived Speech Decoding (SICMD) method, which fuses fMRI and MEG data to decode perceived speech from non‑invasive brain signals. Comprehensive experiments show that SICMD improves Top‑1, Top‑10, and Rankacc scores by over 10%, 10%, and 1.7% respectively, while cutting training costs by 88.8% and 60.5% compared to existing multi‑subject and intra‑subject approaches. Visualizations further confirm the method’s effectiveness.
By Aoke Zhang, Jing Chen
arXiv:2605. 14941v2 Announce Type: replace-cross Abstract: Electroencephalogram (EEG) signals are highly susceptible to artifacts, resulting in a low signal-to-noise ratio, which makes extraction of meaningful neural information challenging.
By Shantanu Sarkar, Jose L. Contreras-Vidal