Magnetoencephalography

Latest papers

14 papers0 papers in the last four weeks, down 100% on the four weeks before. 0.0% of all new papers.

RPA: Residual Patch-Token Adapter for Image Retrieval from EEG and MEG
Sep 19, 2026Yuhui Jin, Yonghao Song, Bingchuan LiuCross-Subject EEG Decoding Cross-Modal Representation Learning
Vortek Lab Inc. · Department of Computer Science and Technology, Tsinghua University · Biology and Biological Engineering, California Institute of Technology
The Semantic Bottleneck: Leveraging Semantic Representations for Non-Invasive Speech Decoding
Sep 9, 2026Gilad D. Landau, Dulhan Jayalath, Oiwi Parker JonesRepresentation Learning Neural Decoding
PNPL University of Oxford
A Roadmap for MEG Foundation Models
Sep 3, 2026Philipp Thölke, Hamza Abdelhedi, Yorguin Mantilla-Ramos +7Magnetoencephalography Neuroimaging
Cognitive and Computational Neuroscience Laboratory (CoCo Lab), Université de Montréal, Montréal, QC, Canada · Department of Psychology, Université de Montréal, Montréal, QC, Canada · Mila – Quebec Artificial Intelligence Institute, Montréal, QC, Canada +10
Interpretable MEG Decoding of Perceived Speech: Cortical Sources and the Stimulus Features That Drive Retrieval
Aug 2, 2026Ilia Semenkov, Daria Kleeva, Ivan Dakhtin +2Neural Decoding Magnetoencephalography
AXXX, Moscow, Russia · HSE University, Moscow, Russia · ITMO University, St. Petersburg, Russia
CORTIVA: Candidate-Score Fusion of Complementary Visual Teachers for EEG- and MEG-to-Image Retrieval
Aug 2, 2026Junhan Wang, Kani ChenEEG-Based Visual Decoding Cross-Modal Alignment
The Hong Kong University of Science and Technology, Hong Kong SAR, China
Heterogeneous Neural Predictivity from Language Models During Naturalistic Comprehension
Jun 25, 2026Xiao JiaNeuroimaging Neural Encoding
School of Artificial Intelligence The Chinese University of Hong Kong, Shenzhen
Bypassing Direct Reconstruction: Speech Detection from MEG via Large-Scale Audio Retrieval
May 13, 2026Boda Xiao, Bo Wang, Heping ChengNeural Decoding Neuroimaging
Center for BioMed-X Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China · Speech and Hearing Research Center, School of Intelligence Science and Technology, Peking University, Beijing, China · National Biomedical Imaging Center, State Key Laboratory of Membrane Biology, Institute of Molecular Medicine, Peking-Tsinghua Center for Life Sciences, College of Future Technology, Peking University, Beijing, China
Zero-Shot Imagined Speech Decoding via Imagined-to-Listened MEG Mapping
May 8, 2026Maryam Maghsoudi, Shihab ShammaBrain-Computer Interfaces Neural Decoding
University of Maryland · College Park, MD 20740
ViBE: Visual-to-M/EEG Brain Encoding via Spatio-Temporal VAE and Distribution-Aligned Projection
Apr 29, 2026Ganxi Xu, Zhao-Rong Lai, Yuting Tang +6Electroencephalography Cross-Modal Alignment
Jinan University, Guangzhou, China · The First Affiliated Hospital of Jinan University, Guangzhou, China · Tsinghua University, Beijing, China +2
MLE-Toolbox: An Open-Source Toolbox for Comprehensive EEG and MEG Data Analysis
Apr 8, 2026Xiaobo LiuMagnetoencephalography Neuroimaging
McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
MEBM-Speech: Multi-scale Enhanced BrainMagic for Robust MEG Speech Detection
Feb 27, 2026Li Songyi, Zheng Linze, Liang Jinghua +1Neural Decoding Speech Processing
Speech and Hearing Research Center, School of Intelligence Science and Technology, Peking University, Beijing, China 100871 · Center for BioMed-X Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China 100871
MEBM-Phoneme: Multi-scale Enhanced BrainMagic for End-to-End MEG Phoneme Classification
Feb 27, 2026Liang Jinghua, Zhang Zifeng, Li Songyi +1Neural Decoding Magnetoencephalography
Speech and Hearing Research Center, School of Intelligence Science and Technology, Peking University, Beijing, China 100871 · Center for BioMed-X Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China 100871
SHINE: Sequential Hierarchical Integration Network for EEG and MEG
Feb 27, 2026Xiran Xu, Yujie Yan, Songyi Li +4Electroencephalography Neural Processes
Speech and Hearing Research Center, School of Intelligence Science and Technology, Peking University, China · National Key Laboratory of General Artificial Intelligence, China · Center for BioMed-X Research, Academy for Advanced Interdisciplinary Studies, Peking University, China
MEG-to-MEG Transfer Learning and Cross-Task Speech/Silence Detection with Limited Data
Feb 20, 2026Xabier de Zuazo, Vincenzo Verbeni, Eva Navas +3Neural Decoding Transfer Learning
HiTZ Center, University of the Basque Country – UPV/EHU, Spain · Basque Center on Cognition, Brain and Language – BCBL, Spain · Laboratory of Functional Anatomy, Faculty of Human Motor Sciences, Université libre de Bruxelles (ULB), Belgium +3

Sep 19, 2026J/K move · Enter open · S save

RPA: Residual Patch-Token Adapter for Image Retrieval from EEG and MEG

Yuhui Jin, Yonghao Song, Bingchuan Liu

Vortek Lab Inc. · Department of Computer Science and Technology, Tsinghua University · Biology and Biological Engineering, California Institute of Technology

Cross-Subject EEG Decoding Cross-Modal Representation Learning EEG-Based Visual Decoding Cross-Modal Retrieval Magnetoencephalography

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Most existing MEG and EEG (M/EEG) visual decoding methods align brain signals with a single global embedding extracted from a pretrained visual encoder, leaving open whether intermediate patch representations, which preserve richer and more granular rich visual information, can improve representation learning. To address this question, we introduce the Residual Patch Adapter (RPA), a lightweight, modular adapter that leverages all patch tokens from an intermediate layer of a ViT visual encoder for alignment. Through extensive ablation analyses, we first show that pooling or masking patch tokens degrades the learned representation, demonstrating that retaining the full set of patch tokens is important for EEG alignment, while the CLS token provides little unique information. We then use a series of six quantitative feature analyses to show that both higher-level semantics and lower-level visual features, including color and texture, are essential for this EEG-to-image alignment. Under current protocols, our system achieves Top-1 accuracies of 95.4% within-subject and 35.5% cross-subject on THINGS-EEG2, and 65.2% and 6.7%, respectively, on THINGS-MEG, achieving state-of-the-art (SOTA) performance across both datasets. Evaluations with alternative brain encoders, including pretrained EEG foundation models, demonstrate that the approach extends beyond the projection-based EEG encoder. Furthermore, we provide a plug-and-play interface that allows RPA to be replaced by convolution, attention, or ConvNeXt alternatives. Together, these findings provide significant insight into M/EEG-to-image representation learning by establishing design principles for leveraging the latent space of visual encoders, and open new directions for brain--image alignment and non-invasive brain--computer interface (BCI).