EEG Signal Generation

EEG: Electroencephalography

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13 papers2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

MontageFlow: Physically Valid High Fidelity EEG Generation
Oct 10, 2026Sudarshan Regmi, Nabaraj Subedi, Arvind Pillai +6EEG Signal Generation Time Series Generation
Dartmouth College
Sensor Geometry as a Flow-Matching Prior for Multi-Channel Brain Signals
Oct 6, 2026Jaedong HwangElectroencephalography EEG Signal Generation
Massachusetts Institute of Technology
GraM-Diff: A Unified Graph-Mamba Diffusion Framework for EEG-Based Alzheimer's Disease Data Generation and Diagnosis
Aug 30, 2026M. Tanveer, Ayush Singh Rana, Sanskriti Jain +5Synthetic Data Augmentation Electroencephalography
Indian Institute of Technology Indore, Indore 453552, Madhya Pradesh, India
Not All EEG Moments Are Equal: Position-Adaptive Time Scheduling for EEG Generation
Jul 25, 2026Boheng Liu, Ziyu Li, Chenghua Duan +2Brain-Computer Interfaces EEG Signal Generation
School of Computer Science and Technology Beijing Institute of Technology Beijing, China
Generative Augmentation for EEG Motor Imagery Classification: A Class-Conditional VAE with Cycle-Consistent Decoder Refinement
Jul 22, 2026Matei Moldoveanu, Alain Sirois, Claire Ben Ali +2Synthetic Data Augmentation MI Classification
Naqi Logix, Paris, France · Inria Center at Univ. Bordeaux / LaBRI, Talence, France · LITIS, INSA Rouen-Normandy, France
Addressing Limited Data in Auditory Attention Decoding with Diffusion Generative Models
Jul 20, 2026David Rannaleet, Victor Gunnarsson, Bo Bernhardsson +2Synthetic Data Augmentation EEG Signal Generation
Department of Automatic Control, Lund University, Lund, Sweden. · Eriksholm Research Centre, Snekkersten, Denmark. · Department of Electrical Engineering, Linköping University, Linköping, Sweden.
B[FM] $^2$ : Brain Foundation Model via Flow Matching with SplitUNet
Jun 18, 2026Jaedong Hwang, Kathleen Zhang, Wei Dai +5Flow Matching EEG Signal Generation
Massachusetts Institute of Technology · KU Leuven
Coordinate-Queryable Neural Field Reconstruction for EEG Spatial Super-Resolution with Unseen-Electrode Generation
Jun 12, 2026Hongjun Liu, Leyu Zhou, Zijianghao Yang +1Electroencephalography EEG Signal Generation
School of Intelligence Science and Technology, University of Science and Technology Beijing · School of Computer and Communication Engineering, University of Science and Technology Beijing
Let EEG Models Learn EEG
May 20, 2026Yifan Wang, Yijia Ma, Wen Li +1Electroencephalography EEG Signal Generation
Stony Brook University · University of Texas Health Center at Houston.
Bridging scalp and intracranial EEG in BCI via pretrained neural representations and geometric constraint embedding
Apr 3, 2026Yihang Dong, Changhong Jing, Shuqiang WangBrain-Computer Interfaces Electroencephalography
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. · University of Chinese Academy of Sciences, Beijing, China.
Cognitive Energy Modeling for Neuroadaptive Human-Machine Systems using EEG and WGAN-GP
Apr 2, 2026Sriram Sattiraju, Vaibhav Gollapalli, Aryan Shah +1EEG Signal Generation Cognitive Modeling
Department of Computer Science, University of Texas at Austin, Austin, TX 78712, USA · Department of Learning Technologies, University of North Texas, Denton, TX 76203, USA
DECODE: Dual-Enhanced Conditioned Diffusion for EEG Forecasting
Feb 25, 2026Mehran Shabanpour, Sadaf Khademi, Konstantinos N Plataniotis +1EEG Signal Generation Time Series Forecasting
Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Canada. · Department of Electrical and Computer Engineering, University of Toronto, Canada
EEGDM: Learning EEG Representation with Latent Diffusion Model
Aug 28, 2025Shaocong Wang, Tong Liu, Yihan Li +6Electroencephalography EEG Signal Generation
Tsinghua University · Tianjin University

Oct 10, 2026J/K move · Enter open · S save

MontageFlow: Physically Valid High Fidelity EEG Generation

Sudarshan Regmi, Nabaraj Subedi, Arvind Pillai, Yu Yvonne Wu+5

Dartmouth College

EEG Signal Generation Time Series Generation Constrained Generative Modeling Flow-Based Generative Modeling Physics-Informed Generative Modeling Conditional Generative Modeling

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Generative modeling offers a promising solution to the scarcity of labeled clinical electroencephalography (EEG) data under strict privacy constraints. Although recent generative models report substantial improvements in generation fidelity, we show that these gains conceal a categorical failure. EEG is not an arbitrary multichannel signal: each channel records the voltage difference between two scalp electrodes. So, real recordings satisfy exact linear identities among their channels. Current generators invariably violate these identities, yet this has gone unnoticed. We therefore propose MontageFlow, a flow-matching model that enforces these identities by restricting its output to signals that satisfy them. We find that ensuring validity improves fidelity too. Furthermore, to retain the EEG characteristics that oversmoothing washes out, we also condition the model on a physiological state vector encoded from the whole EEG window. For generation, this vector is drawn from a prior fitted in latent space. Across 3 large-scale benchmarks, our model improves TS-FID by 1.6-3.6

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over the closest rival while ensuring validity.