Objective.The spinal cord is a vital part of the central nervous system, and its neural signals offer valuable insight into sensory and motor function. Accurate localization of the neural sources that generate spinal cord potentials (SCPs) is essential for advancing both basic research and clinical applications. This study aims to assess the feasibility of applying established EEG source localization methods to simulated SCP data.Approach.We constructed a biophysical model of the upper body and head to simulate surface potentials generated by dipolar sources within the gray matter of the cervical spinal cord. Electrodes were distributed around the neck and upper back to capture these signals. Inverse solutions were obtained using established source localization methods, including sLORETA, and performance was evaluated across varying signal-to-noise ratios (SNRs), electrode layouts, and anatomical model variants.Main results.Regularization parameters between1×10-4and1×10-1yielded the lowest errors, depending on SNR. Under these conditions, predicted source locations were typically within 10 mm of the true source. Higher SNR levels favored larger regularization values. Localization accuracy improved with increasing electrode density, though performance gains plateaued beyond approximately 50% coverage of the neck circumference.Significance.These results demonstrate that established source localization methods can be adapted for spinal cord applications in simulation. The findings highlight the importance of both regularization and sensor configuration, providing a foundation for future improvements in inverse modeling and experimental validation with real SCP recordings.

Premise.Patterns of electrical brain activity recorded via electroencephalography (EEG) offer immense value for scientific and clinical investigations. The inability of supervised EEG encoders to learn robust EEG patterns and their over-reliance on expensive signal annotations have sparked a transition towards general-purpose self-supervised EEG encoders, i.e. EEG foundation models (EEG-FMs), for robust and scalable EEG feature extraction. However, the real-world readiness of early EEG-FMs and the rubrics for long-term research progress remain unclear.Objective.In this work, we conduct a review of ten early EEG-FMs to capture common trends and identify key directions for future development of EEG-FMs.Methods.We comparatively analyze each EEG-FM using three fundamental pillars of foundation modeling, namely the representation of input data, self-supervised modeling, and the evaluation strategy. Based on this analysis, we present a critical synthesis of EEG-FM methodology, empirical findings, and outstanding research gaps.Results.We find that most EEG-FMs adopt a sequence-based modeling scheme that relies on transformer-based backbones and the reconstruction of masked temporal EEG sequences for self-supervision. However, model evaluations remain heterogeneous and largely limited, making it challenging to assess their practical off-the-shelf utility. In addition to adopting standardized and realistic evaluations, future work should demonstrate more substantial scaling effects and make principled and trustworthy choices throughout the EEG representation learning pipeline.Significance.Our review indicates that the development of benchmarks, software tools, technical methodologies, and applications in collaboration with domain experts may advance the translational utility and real-world adoption of EEG-FMs.

Objective.Intrinsic functional brain activity forms a hierarchy, with local neural circuits integrated into large-scale networks. Determining how perturbations to a single brain region can rapidly propagate through this hierarchy and reconfigure global brain state dynamics is essential for decoding neural communication and advancing neuromodulation paradigms. Most prior studies have used perturbations to study trial-averaged responses, which miss moment-to-moment fluctuations, or focused on inter-regional connectivity, which characterizes brain activity as pairwise relationships rather than global states. How focal stimulation reshapes dynamics of global brain states remains unclear.Approach.We bridged this gap by combining transcranial magnetic stimulation (TMS) and electroencephalography (EEG) with microstate analysis, which captures global brain state dynamics with millisecond resolution. In 36 healthy participants, we examined how single-pulse TMS to the dorsolateral prefrontal cortex (DLPFC) and primary motor cortex (M1) reshapes the dynamics of the canonical EEG microstates (A-E) post-pulse. As key nodes of frontoparietal and primary motor networks, respectively, these targets let us investigate how stimulation of functionally distinct regions may differentially induce changes in global brain state dynamics. Analyses were conducted across repeated sessions within participants and validated in an independent dataset to ensure robustness and generalizability.Main results.Compared to pre-pulse baseline, DLPFC stimulation increased the occurrence and transitions of microstates D and E while suppressing those of A and B. These effects replicated across sessions and were validated in an independent dataset. Conversely, M1 stimulation increased microstate A occurrence post-pulse while reducing the occurrence of B. Furthermore, post-pulse microstate dynamics differed significantly from baseline after DLPFC stimulation compared to M1.Significance.These findings demonstrate that focal stimulation induces region-specific effects on global brain state dynamics, providing a mechanistic foundation for neuromodulation strategies to harness site-specific effects on global brain states. Our findings can be leveraged for developing personalized targeted treatments for neuropsychiatric disorders.

Objective.Transcranial direct current stimulation (tDCS) shows potential for cognitive enhancement and neuromodulation, yet its efficacy is limited by substantial inter-individual variability in the induced electric field (E-field) distribution in targeting brain areas and functional networks. This study aims to develop an anatomy-informed framework to select electrode montage and geometry that optimizes network-levelE-field delivery.Approach.Using high-resolution T1-weighted/T2-weighted MRI from 590 participants (ages 36-80) in the Human Connectome Project-Aging, we extracted anatomical features, including cortical, skull, and cerebrospinal fluid (CSF) thickness, and sulcal depth. We simulatedE-fields with finite-element models (SimNIBS 4.1) across multiple commonly used montages in working memory research. Network-level analyses, based on the Schaefer atlas, were performed to assess the spatial distribution and intensity of tDCS-inducedE-fields.Main results.Montage configuration and individual anatomy strongly shape the spatial distribution and intensity of tDCS-inducedE-fields. For montages targeting the dorsolateral prefrontal cortex, the resulting fields extended beyond the intended site and exhibited considerable variability in their cortical focality and magnitude. Principal component and feature importance analyses indicated local gyrification index, cortical thickness, skull thickness, CSF thickness, and sulcal depth as primary determinants of network-levelE-field distribution, with executive and default mode networks most consistently receiving suprathresholdE-field magnitudes. Importantly, peak inducedE-field varied markedly between individuals even under standardized protocols, highlighting substantial inter-individual variability in both dosing and targeting outcomes.Significance.We present a practical workflow for anatomy-guided montage selection that addresses the need for personalized approaches in tDCS. This framework has the potential to improve the efficacy and reproducibility of tDCS in both research and clinical practice by accounting for individual anatomical differences in stimulation planning.

Objective.Machine learning (ML) models are increasingly used to generate electrical stimulation patterns in neuroprosthetic devices such as visual prostheses. While these models promise precise and personalized control, they also introduce new safety risks when model outputs are delivered directly to neural tissue. We propose a systematic, quantitative approach to detect and characterize unsafe stimulation patterns in ML-driven neurostimulation systems.Approach.We adapt an automated software testing technique known as coverage-guided fuzzing to the domain of neural stimulation. Here, fuzzing performs stress testing by perturbing model inputs and tracking whether resulting stimulation violates biophysical limits on charge density, instantaneous current, or electrode co-activation. The framework treats encoders as black boxes and steers exploration with coverage metrics that quantify how broadly test cases span the space of possible outputs and violation types.Main results.Applied to deep stimulus encoders for the retina and cortex, the method systematically reveals diverse stimulation regimes that exceed established safety limits. Two violation-output coverage metrics identify the highest number and diversity of unsafe outputs, enabling interpretable comparisons across architectures and training strategies.Significance.Violation-focused fuzzing reframes safety assessment as an empirical, reproducible process. By transforming safety from a training heuristic into a measurable property of the deployed model, it establishes a foundation for evidence-based benchmarking, regulatory readiness, and ethical assurance in next-generation neural interfaces.