arXiv AI

PHINN-EEG: Topological Time-Series Analysis of Dream-State EEG -- Dynamic Betti Curves for Dream Content Classification and Topology-Conditioned Neural Signal Synthesis

arXiv:2607. 09662v1 Announce Type: cross Abstract: Current electroencephalography (EEG)-based dream detection relies on power spectral density (PSD) and statistical moment features, achieving a state-of-the-art area under the receiver operating characteristic curve (AUC) of approximately 0.

arXiv Machine Learning

Jun 2

Torus Graphs for Large Scale Neural Phase Analysis

arXiv:2606. 00496v1 Announce Type: new Abstract: Oscillatory neural signals such as electroencephalography (EEG) and local field potentials (LFPs) show phase relationships that coordinate communication across brain regions.

By Jack Goffinet, Casey Hanks, David E. Carlson

arXiv Machine Learning

2d ago

EEG-PRISM: Physiologically-Grounded Interpretability of Predictions by EEG Foundation Models

arXiv:2608. 13676v1 Announce Type: new Abstract: Objective: Foundation models represent the next advancement in AI for EEG analysis; however current explainable AI techniques provide attribution scores in the time-channel input space, which is mismatched to clinical intuition about EEG.

By Deeksha M Shama, Punnisa Amornsirikul, Archana Venkataraman

safety

arXiv AI

Jul 10

Omni-Sleep: A Sleep Foundation Model via Hierarchical Contrastive Learning of CNS--ANS Dynamic

arXiv:2607. 07720v1 Announce Type: cross Abstract: Sleep physiology arises from the coordinated dynamics of the central nervous system (CNS) and autonomic nervous system (ANS), as reflected by multimodal polysomnography signals including EEG, EOG, EMG, ECG, and respiration.

By Zhoujie Hou, Song Wang, Kexin Lou, Mo Wang, Chen Wei, Quanying Liu

multimodal benchmarks

arXiv AI

Jul 13

Omni-Sleep: A Sleep Foundation Model via Hierarchical Contrastive Learning of CNS-ANS Dynamics

arXiv:2607. 07720v2 Announce Type: replace-cross Abstract: Sleep physiology arises from the coordinated dynamics of the central nervous system (CNS) and autonomic nervous system (ANS), as reflected by multimodal polysomnography signals including EEG, EOG, EMG, ECG, and respiration.

By Zhoujie Hou, Song Wang, Kexin Lou, Mo Wang, Chen Wei, Quanying Liu

multimodal benchmarks

arXiv Machine Learning

Aug 4

Not All EEG Moments Are Equal: Position-Adaptive Time Scheduling for EEG Generation

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.

By Boheng Liu, Ziyu Li, Chenghua Duan, Qing Li, Xia Wu

diffusion safety

arXiv AI

Jul 20

Learning the Brain's Dynamics as a Port-Hamiltonian System: A GNN-Surrogate Metriplectic Twin for Non-Equilibrium Cortical Dynamics and Closed-Loop Neuromodulation

arXiv:2607. 10439v2 Announce Type: replace-cross Abstract: We model human motor cortex, recorded during rest and motor-imagery BCI conditions, as a port-Hamiltonian system: a conservative interconnection (skew-symmetric coupling between band-limited neural phasors) together with a dissipative port whose state-dependent decay is set by a graph-neural-network surrogate.

By Dibakar Sigdel

arXiv Machine Learning

Jul 15

AnySleep: a channel-agnostic deep learning system for high-resolution sleep staging in multi-center cohorts

arXiv:2512. 14461v2 Announce Type: replace Abstract: Sleep is essential for health, yet studying its dynamics requires manual sleep staging, a labor-intensive step in research and clinical care.

By Niklas Grieger, Jannik Raskob, Siamak Mehrkanoon, Stephan Bialonski

benchmarks

arXiv AI

Jun 19

A Deep Generative Model for Resting-State EEG Synthesis and Transferable Representation Learning

arXiv:2503. 02636v5 Announce Type: replace-cross Abstract: Resting-state EEG provides a non-invasive view of spontaneous brain activity, but extracting meaningful patterns is often limited by scarce high-quality data and reliance on manually engineered features.

By Yeganeh Farahzadi, Morteza Ansarinia, Zoltan Kekecs

diffusion safety

arXiv AI

Jul 24

Transition-Related Potentials as Markers of Narrative Comprehension in Continuous EEG

arXiv:2607. 20720v1 Announce Type: cross Abstract: Harnessing the potential of electroencephalography (EEG) for brain research is fundamentally limited by intrinsic noise and the diffuse projection of brain-generated activity over the scalp.

By B\'alint Csan\'ady, P\'eter Vedres, Krist\'of Zsolt Mak\'o, Orsolya Papp-Zipernovszky, M\'arta Volosin, D\'avid Apagyi, Andr\'as Luk\'acs, Andr\'as B\'alint Kov\'acs, Zoltan Nadasdy

arXiv Machine Learning

Jul 27

Interpretable EEG biomarkers with bag-of-waves: Spatial and temporal waveform dictionaries for low-data regimes

arXiv:2607. 22508v1 Announce Type: new Abstract: Electroencephalography (EEG) is widely used to diagnose neurological conditions, but its analysis usually relies on either predefined spectral features or deep neural networks.

By Athanasios Papastathopoulos-Katsaros, Steven T. Lee, Lin Yao, Ajay Thomas, Junseok Park, Matthew J. McGinley, Zhandong Liu

benchmarks safety

arXiv Machine Learning

Aug 11

Cognitive Energy Modeling for Neuroadaptive Human-Machine Systems using EEG and WGAN-GP

arXiv:2604. 01653v2 Announce Type: replace Abstract: Electroencephalography (EEG) provides a non-invasive insight into the brain's cognitive and emotional dynamics.

By Sriram Sattiraju, Vaibhav Gollapalli, Aryan Shah, Timothy McMahan

diffusion

arXiv AI

Aug 10

A Physics-Inspired Classical Digital Twin of Cortical Dynamics: A Band-Stratified Metriplectic Port-Hamiltonian Neural Network Learned from Brain-Computer-Interface EEG

arXiv:2607. 10439v3 Announce Type: replace-cross Abstract: We present a physics-inspired classical digital twin of brain-computer- interface (BCI) data: a graph neural network constrained to a band-stratified, metriplectic port-Hamiltonian form, with parameters learned from scalp EEG recorded during rest and motor imagery.

By Dibakar Sigdel

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