arXiv subjects

Yifei Jia

Publications and source records attributed to Yifei Jia.

8 recordsLinked to original sources

arXiv2026

Design and Validation of a Portable EEG-tES Platform Supporting High-Rate EEG Recording and Temporal Interference Stimulation

Background: Closed-loop neuromodulation integrating electroencephalography (EEG) and transcranial electrical stimulation (tES) has strong potential for neuroscience research and clinical applications. However, existing platforms often rely on benchtop instrumentation or FPGA-centered architectures, limiting portability and increasing system complexity. Methods: We developed a compact wearable bidirectional platform integrating 8-channel EEG acquisition and 2-channel tES within a single microcontroller unit (MCU). An ADS1299-based front-end supports up to 8 kHz sampling per channel for simultaneous neural and stimulation signal recording. The stimulation module uses direct digital synthesis (DDS) technique to generate programmable tDCS, tACS, and tTIS waveforms, with optimized firmware design enabling real-time operation on the microcontroller. Results: Experimental evaluation demonstrated high signal fidelity, with EEG correlation coefficients reaching $99\%$ under controlled conditions and $93.5\%$ on a gelatine head phantom. Stimulation performance showed current errors below $1\%$ across tDCS, tACS, and tTIS modes. The platform also reliably recorded concurrent EEG--tES signals without saturation during high-amplitude and high-frequency stimulation. Conclusion: These findings demonstrate that an MCU-centered architecture can effectively support simultaneous EEG sensing and multimodal tES delivery in a compact wearable form factor, while maintaining accurate stimulation and robust signal acquisition. Significance: This work provides a practical foundation for portable closed-loop neuromodulation systems with integrated host software, expanding access to personalized stimulation paradigms, and future point-of-care neurotechnology applications.

eess.SP↗

arXiv2026

Local Fokker--Planck Geometry for Score Estimation: Heat-Ball Mean-Value Representations and Exact High-Dimensional Sampling

Score-based generative models and Langevin samplers rely on estimating the score function $\nabla_x\log p_t(x)$ of a forward diffusion. Classically this is tractable when the drift is linear: the marginal density is Gaussian and the score is a global conditional expectation. For a general nonlinear, state-dependent drift the marginal density has no closed form, and existing methods--denoising score matching and global Fokker--Planck residual penalties--resort to global averaging that inflates estimation error in low-density regions precisely where accuracy is most critical. We address this by developing a local Fokker--Planck geometric framework that replaces global conditioning with local parabolic averaging. Our approach rests on three contributions. First, a time change to the cumulative-variance coordinate reduces the variable-coefficient Fokker--Planck equation to a standard inhomogeneous heat equation, on which we extend Evans' classical heat-ball monotonicity method to derive exact local mean-value representations for the score $\nabla_x\log p$ together with the density, log-density, and entropy density; local well-posedness is established under an explicit dimension-dependent drift budget. Second, for high-dimensional Monte Carlo evaluation of the resulting heat-ball integrals, we introduce the $κ$-measure and derive its exact factorized sampler with unit per-sample weight, $χ^2_2$ radial concentration. Third, the $r\to0$ limit of the heat-ball residual recovers the pointwise Fokker--Planck residual, showing that the local framework is a one-parameter generalization of global FP-residual methods, and that the DSM population minimizer is feasible for the heat-ball constraint at every scale. We validate the framework on 2D structured data on 256-dimensional MNIST, and on a dedicated sampler study confirming the concentration laws.

stat.ML↗

arXiv2026

Non-flat Ekman Boundary Layers: Topographic Lift, Generalized Ekman Pumping, and Anisotropic Asymptotic Behavior

The Ekman boundary layer, a fundamental concept in geophysical fluid mechanics, describes the near-boundary fluid motion subject to rotation. Within the singular limit framework of rapid rotation and vanishing viscosity, classical studies of Ekman theory (e.g., Desjardins and Grenier (1999), Masmoudi (2000)) are predominantly restricted to flat or small-amplitude boundary assumptions. The conventional flat-boundary assumption obscures the complex mechanisms induced by topographic curvature; moreover, even small-amplitude perturbations reduce topographic effects to simple linear forcing terms. Consequently, this paper investigates the singular limit behavior of rotating fluids over a non-flat boundary $z=B(x,y)$ of $\mathcal{O}(1)$ amplitude with uniformly bounded slope and curvature. We elucidate how such topography modulates fluid dissipation through two distinct mechanisms: macroscopic topographic forcing and microscopic anisotropic pumping. First, using multi-scale asymptotic analysis, we construct a class of approximate solutions that explicitly depend on the boundary's geometric characteristics, yielding a two-dimensional limit system fundamentally distinct from classical models. A key innovation of this system is the introduction of a generalized velocity field defined via the topographic metric tensor. This formulation not only generalizes the traditional isotropic linear damping to anisotropic geometric damping but also couples rotational effects to macroscopic vertical acceleration. Furthermore, using energy methods, we establish the $L^2$ convergence of these variable-thickness approximate solutions to the weak solutions of the original three-dimensional system. Finally, we analyze the multiple mechanisms governing rotating fluid motion over large-amplitude topography using a representative class of boundary geometries.

math.AP↗

arXiv2025

Geometry-dependent Ekman layer approximations on curved domains: L^{\infty} convergence

The Ekman boundary layer is a fundamental concept in fluid dynamics that describes fluid motion near boundaries affected by Earth's rotation. Most theoretical studies have simplified their analysis by assuming a planar boundary surface, resulting in limited exploration of structures with general smooth boundary conditions. Investigating the impact of boundary geometry in the Ekman boundary layer is essential, as initially suggested by J.L. Lions and further examined in Masmoudi's study [Comm. Pure Appl. Math. 53 (2000), 432-483] under small amplitude periodic boundary conditions. This paper clarifies how boundary geometry influences flow fields and characterizes its effects on near-boundary layer flow. We construct a class of multi-scale approximate solutions based on the boundary's geometric features and establish their convergence in the L^{\infty} framework. Our findings do not require a small-amplitude assumption, only an upper bound on the Gaussian curvature of the boundary surface. Notably, when the boundary is planar, our approach aligns with existing studies. Additionally, in the vanishing-viscosity limit, we derive a limiting-state system dependent on boundary geometric parameters. These contributions extend the theoretical understanding of boundary-layer interactions to general curved geometries and have possible applications in atmospheric, oceanic, and other geophysical flow contexts.

math-ph↗

arXiv2025

TemporalFlowViz: Parameter-Aware Visual Analytics for Interpreting Scramjet Combustion Evolution

Understanding the complex combustion dynamics within scramjet engines is critical for advancing high-speed propulsion technologies. However, the large scale and high dimensionality of simulation-generated temporal flow field data present significant challenges for visual interpretation, feature differentiation, and cross-case comparison. In this paper, we present TemporalFlowViz, a parameter-aware visual analytics workflow and system designed to support expert-driven clustering, visualization, and interpretation of temporal flow fields from scramjet combustion simulations. Our approach leverages hundreds of simulated combustion cases with varying initial conditions, each producing time-sequenced flow field images. We use pretrained Vision Transformers to extract high-dimensional embeddings from these frames, apply dimensionality reduction and density-based clustering to uncover latent combustion modes, and construct temporal trajectories in the embedding space to track the evolution of each simulation over time. To bridge the gap between latent representations and expert reasoning, domain specialists annotate representative cluster centroids with descriptive labels. These annotations are used as contextual prompts for a vision-language model, which generates natural-language summaries for individual frames and full simulation cases. The system also supports parameter-based filtering, similarity-based case retrieval, and coordinated multi-view exploration to facilitate in-depth analysis. We demonstrate the effectiveness of TemporalFlowViz through two expert-informed case studies and expert feedback, showing TemporalFlowViz enhances hypothesis generation, supports interpretable pattern discovery, and enhances knowledge discovery in large-scale scramjet combustion analysis.

cs.CV↗

arXiv2025

Neuro-MoBRE: Exploring Multi-subject Multi-task Intracranial Decoding via Explicit Heterogeneity Resolving

Neurophysiological decoding, fundamental to advancing brain-computer interface (BCI) technologies, has significantly benefited from recent advances in deep learning. However, existing decoding approaches largely remain constrained to single-task scenarios and individual subjects, limiting their broader applicability and generalizability. Efforts towards creating large-scale neurophysiological foundation models have shown promise, but continue to struggle with significant challenges due to pervasive data heterogeneity across subjects and decoding tasks. Simply increasing model parameters and dataset size without explicitly addressing this heterogeneity fails to replicate the scaling successes seen in natural language processing. Here, we introduce the Neural Mixture of Brain Regional Experts (Neuro-MoBRE), a general-purpose decoding framework explicitly designed to manage the ubiquitous data heterogeneity in neurophysiological modeling. Neuro-MoBRE incorporates a brain-regional-temporal embedding mechanism combined with a mixture-of-experts approach, assigning neural signals from distinct brain regions to specialized regional experts on a unified embedding basis, thus explicitly resolving both structural and functional heterogeneity. Additionally, our region-masked autoencoding pre-training strategy further enhances representational consistency among subjects, complemented by a task-disentangled information aggregation method tailored to effectively handle task-specific neural variations. Evaluations conducted on intracranial recordings from 11 subjects across five diverse tasks, including complex language decoding and epileptic seizure diagnosis, demonstrate that Neuro-MoBRE surpasses prior art and exhibits robust generalization for zero-shot decoding on unseen subjects.

eess.SP↗

arXiv2025

Towards Unified Neural Decoding with Brain Functional Network Modeling

Recent achievements in implantable brain-computer interfaces (iBCIs) have demonstrated the potential to decode cognitive and motor behaviors with intracranial brain recordings; however, individual physiological and electrode implantation heterogeneities have constrained current approaches to neural decoding within single individuals, rendering interindividual neural decoding elusive. Here, we present Multi-individual Brain Region-Aggregated Network (MIBRAIN), a neural decoding framework that constructs a whole functional brain network model by integrating intracranial neurophysiological recordings across multiple individuals. MIBRAIN leverages self-supervised learning to derive generalized neural prototypes and supports group-level analysis of brain-region interactions and inter-subject neural synchrony. To validate our framework, we recorded stereoelectroencephalography (sEEG) signals from a cohort of individuals performing Mandarin syllable articulation. Both real-time online and offline decoding experiments demonstrated significant improvements in both audible and silent articulation decoding, enhanced decoding accuracy with increased multi-subject data integration, and effective generalization to unseen subjects. Furthermore, neural predictions for regions without direct electrode coverage were validated against authentic neural data. Overall, this framework paves the way for robust neural decoding across individuals and offers insights for practical clinical applications.

q-bio.NC↗

arXiv2021

Stop Line Aided Cooperative Positioning of Connected Vehicles

This paper develops a stop line aided cooperative positioning framework for connected vehicles, which creatively utilizes the location of the stop-line to achieve the positioning enhancement for a vehicular ad-hoc network (VANET) in intersection scenarios via Vehicle-to-Vehicle (V2V) communication. Firstly, a self-positioning correction scheme for the first stopped vehicle is presented, which applied the stop line information as benchmarks to correct the GNSS/INS positioning results. Then, the local observations of each vehicle are fused with the position estimates of other vehicles and the inter-vehicle distance measurements by using an extended Kalman filter (EKF). In this way, the benefits of the first stopped vehicle are extended to the whole VANET. Such a cooperative inertial navigation (CIN) framework can greatly improve the positioning performance of the VANET. Finally, experiments in Beijing show the effectiveness of the proposed stop line aided cooperative positioning framework.

cs.RO↗