The paper introduces a Cross-Subject Perceived Speech Decoding (CPSD) framework that tackles the challenge of decoding perceived speech from non‑invasive brain recordings across different subjects. CPSD uses a two‑stage training process: first, contrastive learning pre‑trains a source model on multiple subjects to capture shared representations; second, personal specialization fine‑tunes the model for a target subject by extracting consistent components and further training on that subject’s data. A Positional Encoding‑based Spatial Attention (PESA) module is added to remap MEG/EEG data into a standardized reference space, improving cross‑subject consistency. Evaluations on three datasets (Armeni 2022, PKUEEG 2025, Broderick 2018) show that CPSD outperforms baseline methods by more than 6.8%, 15.4%, and 15.8% in Top‑10 accuracy, demonstrating its effectiveness, efficiency, and robustness.
By Aoke Zhang, Bo Wang, Xihong Wu, Heping Cheng, Jing Chen
arXiv:2608. 01481v1 Announce Type: new Abstract: Short segments of perceived speech can be retrieved from non-invasive magnetoencephalographic (MEG) recordings by deep networks trained with a CLIP-style objective against wav2vec 2.
By Ilia Semenkov, Daria Kleeva, Ivan Dakhtin, Zarina Maksudova, Alex Ossadtchi
The paper introduces CoMA-DiT, a bidirectional cross‑modal Diffusion Transformer that uses paired modalities as mutual generative supervision for latent augmentation rather than just inputs for fusion. By conditioning velocity prediction on the paired modality through cross‑modal attention and injecting variation via a reliability‑gated residual mechanism, CoMA‑DiT improves multimodal brain state decoding. Experiments on auditory attention decoding and emotion recognition show consistent gains over 20 baselines, with absolute accuracy and macro‑F1 improvements of 4.28% and 6.70% respectively, and extensive analyses confirm its robustness and interpretability.
By Ziwei Wang, Xingyi He, Hongbin Wang, Tianwang Jia, Bohan Fang, Dongrui Wu
Short segments of perceived speech can be retrieved from non-invasive magnetoencephalographic (MEG) recordings by deep networks trained with a CLIP-style objective against wav2vec 2. 0 audio embeddings.
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
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:2607. 19394v1 Announce Type: cross Abstract: Generalizing across subjects remains challenging in invasive neural recordings because electrode configurations, anatomical structures, and neural signal patterns vary substantially across individuals.
By Ji-Hoon Heo, Aleksandra Joanna Wisniewska, Seo-Hyun Lee, Seong-Whan Lee
arXiv:2605. 00025v3 Announce Type: replace-cross Abstract: Speech neuroprosthesis systems decode intended speech from neural activity in the absence of audible output, offering a path to restoring communication for individuals with speech-impairing conditions.
By Yuanhao Chen, Peter Chin
arXiv:2510. 15371v2 Announce Type: replace-cross Abstract: Classification of electroencephalogram (EEG) signals obtained during motor imagery (MI) has substantial application potential, including communication assistance and rehabilitation support for patients with motor impairments.
By Shuntaro Suzuki, Shunya Nagashima, Komei Sugiura
The article outlines the emerging field of Magnetoencephalography (MEG) foundation models, explaining how these reusable, pretrained models can surpass traditional task‑specific decoding pipelines. It reviews current design choices—such as tokenization, sensor versus source representations, and self‑supervised objectives—and notes the limited number of existing MEG‑specific models and datasets. The authors propose a roadmap that includes native MEG pretraining, adaptation of EEG models, transfer from generic time‑series models, and multimodal integration with other neuroimaging and behavioral data, while emphasizing the need for coordinated infrastructure, rigorous evaluation, and responsible data‑sharing practices.
By Philipp Th\"olke, Hamza Abdelhedi, Yorguin Mantilla-Ramos, Fouad Lbakali, Oumayma Gharbi, Catherine Duclos, Annalisa Pascarella, Vanessa Hadid, Oiwi Parker Jones, Karim Jerbi
arXiv:2609.24095v1 Announce Type: new
Abstract: While deep learning has enabled language decoding from intracranial brain recordings, extending this capability to non-invasive recordings remains an u...
By Yueyang Li, Shuran Chen, Wai Ting Siok, Nizhuan Wang
RAMamba-Net is a new multimodal fusion network designed for auditory attention decoding (AAD) that combines EEG and electrooculography (EOG) signals. It uses a Mamba-enhanced band-aware convolutional Transformer to capture EEG band-specific patterns and long-range temporal dynamics, while a dual-branch encoder models EOG temporal and inter-channel dependencies. Cross‑modal attention and a reliability‑aware module estimate sample‑wise modality weights, improving fusion robustness and achieving a 5.76% accuracy gain over unimodal baselines on two AAD benchmarks.
By Xingyi He, Ziwei Wang, Dongrui Wu