The paper introduces a compact, dispersive RC circuit model for electro-quasi-static (EQS) head modeling, accurately representing the brain, skull, and scalp layers for brain-oriented applications.

The paper proposes a multi-dimensional evaluation framework to assess EEG foundation models under realistic low-resource conditions, finding that while these models excel in long-context tasks, their…

The paper proposes a compact magnetic tunnel junction (MTJ) device with orthogonal easy axes to implement signed leaky integrate-and-fire (LIF) neurons, enabling bipolar spike generation for enhanced…

The paper introduces a design-oriented methodology and a closed-form macromodel to quantify how noise coupled through Through-Silicon Vias (TSVs) degrades the spectral purity of sensitive RF oscillato…

This paper benchmarks five positional encoding strategies for transformer-based EEG foundation models, concluding that the optimal encoding is task-dependent and no single strategy is universally supe…

The paper designed a minimalist BCMI system to translate EEG-measured emotional valence into adaptive music, but preliminary testing showed that frontal alpha asymmetry was not reliably modulated by i…

The paper introduces Dual-Spectral Flow Matching (DSFM), a novel generative framework that uses wavelet and cosine transforms to synthesize highly realistic, non-stationary fMRI time series for improv…

This paper investigates a novel vulnerability in tactile sensing by demonstrating that targeted Electromagnetic Interference (EMI) can induce strong, misleading 'phantom forces' in Hall-effect fingert…

The paper proposes a Ferroelectric Charge-Domain Compute Cell (FCDC) using HZO memcapacitors to perform attention computation, achieving significant energy efficiency gains, especially for long-reside…

The paper proposes MITL, an MsFEM-inspired transfer learning strategy for CNN-based reduced-order models, enabling efficient and adaptable approximation of multiscale systems with minimal retraining.

The paper introduces SpikeWFM, a novel hybrid architecture combining spiking neural networks (SNNs) and transformers, which significantly improves the robustness and accuracy of wireless foundation mo…

The paper introduces Cellular Sheaf Neural Operators, a discretization-aware framework that models constrained PDEs by representing physical states on oriented cell complexes to enforce structure-pres…

The paper demonstrates that quadratic integrate-and-fire (QIF) neurons are superior to leaky integrate-and-fire (LIF) neurons for gradient descent training in spiking neural networks because their con…

This paper introduces a robust magneto-thermal surface contact approximation (SCA) that efficiently models the electrical and thermal behavior of turn-to-turn contact layers in no-insulation HTS coils…

The paper introduces PIGMENT, a physics-informed foundation model that enables reliable quantitative mapping of brain microstructure from extremely sparse or challenging diffusion MRI scans.

The paper introduces the EXTRA homogenization method, which enables accurate and computationally efficient 3D magneto-thermal finite-element simulation of large-scale HTS magnets by selectively resolv…

The paper proposes using a Physics-Informed Neural Network (PINN) residual as an efficient, physics-guided indicator to guide adaptive mesh refinement (AMR) for classical finite-difference PDE solvers…

This paper develops a supervised machine learning surrogate model, using a neural network, to predict the effective Lamé parameters of hyperelastic composites based on low-dimensional microstructural…

The paper introduces a subgrid marching tetrahedra scheme that accurately recovers complex, intersection-free manifold meshes from tetrahedral grids, overcoming limitations of classic marching methods…

This study compares multiple post-hoc explainable AI methods (e.g., DeepSHAP, GradCAM) to interpret how deep learning models use EEG data to detect Major Depressive Disorder, finding that while method…