Journal of Neuroscience Methods

Stereotaxic brain surgery is a foundational neurosurgical technique used to deliver chemical, pharmacological, or genetic material to specific brain regions, or to precisely implant electrodes and stimulators. Accurate targeting depends on reliable identification of skull landmarks, particularly bregma and lambda. Here we present a photogrammetry-based automated small animal stereotaxic platform that uses a single, freely handheld camera, such as a standard smartphone, to generate high-resolution, polychromatic 3D skull reconstructions. Because photogrammetry requires no fixed overhead hardware, the surgical field remains fully accessible for instruments, microscopes, and other equipment. The resulting color reconstructions substantially improve identification of bregma and lambda compared to monochromatic approaches, and the higher spatial resolution translates directly into improved targeting accuracy and surgical speed. The system operates by a user moving a handheld camera around the exposed skull. The platform automatically produces a detailed 3D mesh and computes stereotaxic coordinates without manual measurement. Together, these properties yield a practical, accessible yet accurate platform for automated small animal neurosurgery. We previously described a structured illumination approach in combination with a Steward platform; however, that system required a fixed projector and camera array that occupied critical surgical workspace and produced monochromatic meshes that complicated landmark identification. The photogrammetry-based method described here overcomes both limitations while retaining full compatibility with the Steward platform as a stereotaxic base.

Vision is traditionally studied using simplified stimuli presented briefly near the center of a flat display while subjects maintain visual fixation. This paradigm contrasts sharply with real-world vision, which is immersive, dynamic and strongly entrained to self-initiated actions. Traditional approaches have allowed researchers to systematically study the neural encoding of visual features and the influence of cognitive operations such as attention. However, other methods are needed to study more holistic and first-person perspectives on vision, such as those related to physical space, continuous time, and self-movement. To enable the study of these and other aspects of real-world vision, we developed hemispherical ("dome") display systems for macaque visual neuroscience. For functional MRI experiments, a compact rear-projection dome display fits within the bore of a clinical MRI scanner. For electrophysiological recordings, a larger front-projection dome display is illuminated from above using a spherical mirror. Both setups enable complete and dynamic stimulation of approximately 180{degrees} of the subjects field of view, thus facilitating studies requiring visual immersion. To ensure accurate angular geometry across the hemispherical display, we present unified rendering and calibration software that supports natural fisheye videos, conventional visual stimuli, and virtual 3D environments. The calibration procedure automatically compensates for projector, mirror, and dome distortions through geometric pre-warping, ensuring correct visual-angle representation across the display. Pilot fMRI experiments demonstrate robust activation of peripheral visual cortex during both conventional pattern stimulation and naturalistic self-movement. Together, these dome systems provide a flexible platform for investigating aspects of vision that are difficult to study with conventional displays, including peripheral processing, visual immersion, optic flow, self-motion, and holistic scene perception.

Rodent brain matrices that produce coronal or sagittal brain sections for histology confer reproducibility and enable high throughput processing of tissues. However, a stainless steel or acrylic brain matrix that produces tissue sections in a transversal (or horizontal) orientation is currently unavailable as a standard tool. This limits the direct comparison of bilateral brain hemispheres within a single histological section, as freehand trimming to obtain horizontal planes is not easily replicable across samples. To mitigate this challenge, we designed a low-cost (USD 7 per unit), 3D-printed resin-based transverse brain matrix that accommodates mouse brains ranging from 12 to 16 mm in length from the olfactory bulb to the brainstem. Our matrix reproducibly generates horizontal tissue sections with a minimum of 1-mm-thickness without causing visible tissue deformation, which is comparable to the performance of commercial rodent brain matrices. Users may adapt the accompanying CAD code using our video tutorials to customize the transverse brain matrix for their specific needs, including alternative brain size, shape, and tissue thickness.

Head-fixed behavior in rodents is a foundational technique in systems neuroscience which enables use of sophisticated imaging techniques in combination with animal behavior. However, accessibility of head-fixed behavior techniques is limited. Animal training consumes a large amount of experimenter labor and commercial setups, when available, are largely inflexible and financially burdensome. Here, we present a low-cost, modular, and open-source hardware and software implementation for head-fixed rodent decision-making tasks. Our design lowers experimenter labor and enables large teams of researchers to participate in animal training with minimal experimenter error using a simple touchscreen GUI and automated training progression. We demonstrate the efficacy of the platform by training a cohort of animals in a two-choice probabilistic rapid-reversal task in which mice continuously update action choices based on recent reward history. The presented design lowers the barrier to entry for laboratories seeking to conduct head-fixed rodent behavior and provides modular solutions for developing custom rigs based on experimental demands.

Defining the boundaries of functional cortical areas is increasingly important for targeted electro-physiology, optical imaging, viral delivery, and circuit manipulation. Intrinsic optical signal imaging (IOSI) provides a rapid and minimally invasive approach for mapping stimulus-evoked cortical activity, but its implementation often requires laboratory-specific combinations of stimulus-generation hardware, experiment-control software, synchronization devices, and data-acquisition systems. These requirements limit accessibility and hinder the use of IOSI as a routine functional mapping tool. Here, we present the Arduino-Based Intrinsic Stimulation System (ABISS), an open-source platform that integrates auditory and visual stimulus generation, trial timing, and image-acquisition triggering into a single programmable device. ABISS generates auditory tone stimuli, VGA-based visual stimuli, and tightly synchronized camera-trigger pulses without requiring a dedicated experiment-control system. Stimulus protocols are also fully modifiable in firmware. Performance was evaluated in auditory and visual cortices of mice. Engineering validation demonstrated accurate stimulus generation and synchronization between stimulus delivery and camera triggering over extended recording sessions. Biological validation showed that ABISS output results in auditory and visual intrinsic signal maps comparable to those obtained using highly specialized or commercial platforms. Together, these findings demonstrate the utility of a low-cost open-source platform for experimental control of intrinsic optical signal imaging. By reducing the technical and financial barriers associated with routine intrinsic optical imaging, ABISS facilitates broader adoption of functional cortical mapping as a tool for improved cortical localization in neuroscience experiments. Significance StatementFunctional cortical mapping is an increasingly important element of neuroscience experimental design as anatomical coordinates alone are often insufficient for defining cortical boundaries in individual animals. Intrinsic optical signal imaging provides an effective solution but traditionally requires specialized hardware, commercial stimulus-generation systems, and laboratory-specific synchronization workflows. We developed ABISS, an inexpensive, open-source platform that integrates auditory and visual stimulus generation with synchronized camera triggering in a single programmable device. ABISS produces functional cortical maps comparable to those obtainable with commercial or specialized systems while substantially reducing hardware complexity and cost. By making intrinsic optical signal imaging more accessible, ABISS lowers the practical barriers for routine functional mapping of the brain and promotes adoption of this important neuroscience technique.

Background Accurate MRI-guided stereotaxic targeting in nonhuman primates requires reliable registration between MRI coordinates and stereotaxic space. We evaluated skull-fixed MR-compatible screw fiducials as a practical reference for this registration. Results Using a 3D-printed skull model with 28 fiducials and three internal targets, we compared offset translation, rigid registration, and affine registration under aligned and misaligned conditions. Offset translation was most sensitive to misalignment. Affine registration fit skull-surface fiducials closely, but rigid registration produced lower overall target errors. In vivo validation showed that implanted fiducials could be identified on MRI and re-measured during a later surgery. Comparison with existing methods This approach builds on previous fiducial-based stereotaxic methods by systematically comparing registration transforms, testing misalignment, evaluating fiducial number, and measuring accuracy at independent internal targets. Conclusions Skull-fixed fiducials provide a practical framework for MRI-to-stereotaxic registration. Rigid registration may offer the safest balance between correcting positioning differences and preserving accuracy at internal targets.

IntroductionThe insula is an increasingly important target for functional neurosurgery given its involvement in a range of neurological and neuropsychiatric disorders, including epilepsy and chronic pain. As this practice evolves, optimal targeting will require standardized outcome measures that relate electrode or laser trajectory to postprocedural outcome. Traditional whole- brain registration approaches fail to capture the substantial person-to-person variability in insular gyral configuration, including the relative internal rotation of the insular gyri with respect to standard stereotactic space. ObjectiveWe propose and validate a stereotactic coordinate system based on local anatomical landmarks to facilitate surgical planning and standardized outcome assessment within the insular cortex. MethodsOur approach transforms brain MRI first into standard AC-PC space, and then into an insular-specific space defined by five anatomical landmarks: four points along the central sulcus of the insula and one point at the middle cerebral artery (MCA) bifurcation (at the limen insulae). The system calculates two angles - {theta} (axial) and {varphi} (sagittal) - between the AC-PC line and the insular axis, and the brain volume undergoes sequential rotation through these angles followed by translation to place the coordinate systems origin along the insular axis. ResultsIn a sample of 32 patients, the angle between the AC-PC line and the insular axis ranged from -17{degrees} to 17{degrees} in the axial plane ({theta}) and 31{degrees} to 69{degrees} in the sagittal plane ({varphi}). In the resulting coordinate system, the insular axis defines z = 0 and the MCA turning point defines y = 0. We developed a custom, open-access MATLAB graphical interface that allows intuitive implementation of this system for both surgical planning and postoperative analysis; implanted electrodes, laser fiber position, and ablation geometry can each be localized within this common space. As a demonstration of its utility for pooling data across subjects, we applied the transformation to a previously acquired intracranial electrophysiology dataset and found that anatomically consistent, effector-specific motor representations emerged across 18 subjects once electrode positions were expressed in insular-specific coordinates. ConclusionAs stereotactic surgery for insular targets becomes more common with expanding scientific inquiry, an insular-specific coordinate system may facilitate operative planning and functional mapping, and may help standardize outcome assessment across patients and institutions. SIGNIFICANCE STATEMENTThe insular cortex represents an increasingly important surgical target for therapeutic interventions, yet substantial person-to-person anatomical variability hampers standardized targeting and outcome comparison. The insula is simultaneously the subject of expanding scientific inquiry -- into interoception, pain, autonomic regulation, salience processing, and sensorimotor representation -- much of it now pursued through intracranial recording and stimulation in humans, where cohorts are small, electrode sampling is idiosyncratic, and progress therefore depends on pooling data across patients in a frame that respects insular gyral architecture. We present "Insulotaxy," a stereotactic coordinate system built from consistent, easily identifiable local anatomical landmarks that accounts for the insulas unique rotational relationship to standard brain coordinates. An open-source MATLAB tool transforms imaging into insular-specific coordinates, facilitating surgical planning for ablation and electrode placement while enabling standardized outcome reporting across institutions. By providing locally anchored, anatomically aligned coordinates rather than relying on whole-brain registration, this framework addresses a practical gap in functional neurosurgery and lays a foundation for pooling clinical and electrophysiological data as insular interventions become more prevalent.

Electroencephalography (EEG) is fundamental to cognitive neuroscience as it provides a direct measure of human neural activities with millisecond precision. Its noninvasive nature allows for the study of brain function across diverse age groups and experimental contexts--from newborns to adults, and from tightly controlled laboratory environments to more naturalistic real-world settings. However, EEG signals--especially those recorded from infants--are highly prone to noise and arti-facts, posing significant challenges for data analysis. To address these issues, we present APICE-Py (Automated Preprocessing for Infants Continuous EEG), an open-source preprocessing pipeline originally designed as a matlab toolbox for infant EEG and now re-implemented in Python to support scalable and flexible analysis across developmental and adult datasets. APICE-Py is built upon three core principles: (i) adaptive artifact detection on continuous data using data-driven thresholds rather than fixed cutoffs; (ii) hierarchical artifact correction, combining short-segment correction via Principal Component Analysis (PCA) with broader segment and continuous data correction using Spherical Spline Interpolation (SSI); and (iii)transparent reporting, providing comprehensive quality logs and decision-tracking to ensure reproducibility and informed analysis. We summarize the underlying algorithms, release an implementation compatible with common EEG formats, and demonstrate its use on three datasets spanning neonates, 5-month-old infants, and child-parent hyperscanned data, acquired using high-density wet electrodes and mobile gel-based EEG systems. When benchmarked against the original MATLAB implementation, APICE-Py achieved comparable levels of data quality and trial retention. While the original pipeline was developed for early developmental EEG (e.g., infants), we show that the pipeline further extends its applicability to both children and adult datasets, enabling robust preprocessing across a broad age range. Moreover, it supports data acquired using a variety of EEG configurations and experimental settings, highlighting its flexibility across age groups, hardware systems, and paradigms. The APICE-Py source code and documentation are freely available at https://github.com/neurokidslab/apice-py.

In recent years, rodent touchscreen-based paradigms have gained popularity for their flexible task design, automated data collection and improved standardization. These devices minimize experimenter intervention and contribute to more replicable and reliable research. However, the high costs and proprietary hardware / software associated with commercially available systems present a financial barrier for smaller labs or researchers with limited resources. To address this issue, we developed "NC4Touch", an open-source scalable, modular rodent testing apparatus. It features three independent touchscreens and a feeding port for easy use in a wide array of tests comparable to those available with standard operant chambers, with the added benefit that visual stimuli can be highly customized. The system includes a user-friendly graphical interface that offers real-time control, task customization, video recording, and data management. We demonstrate its effectiveness in a visual discrimination task using both rats and mice. Several devices can be operated in parallel from the same computer and user interface, allowing high- throughput data collection. We provide detailed assembly instructions for the hardware and well- documented and easily-configurable software. We hope that the affordable open-source nature of NC4Touch will expand the scope of behavioral testing, allowing researchers to overcome traditional financial barriers and create a more collaborative community. Significance StatementTouchscreen-based testing is a powerful tool for assessing cognition in rodent models, however commercial systems remain cost-prohibitive for many researchers. NC4Touch is a rodent touchscreen apparatus that provides an open-source, customizable, and affordable alternative that enables high-throughput, cognitive testing in both mice and rats. By lowering financial barriers and promoting hackability, this system enables greater accessibility and collaboration in research.

Background: Transcardial perfusion fixation is widely used to preserve rodent brains for histological and ultrastructural analysis, but protocols can vary in the use of pre-fixation washout, fixative formulation, and osmotic additives. Relatively few studies have directly compared how the chemical composition of the perfusate affects tissue preservation. Methods: Male Sprague-Dawley rats underwent transcardial perfusion using a series of aldehyde-based fixative formulations that varied in fixative composition, use of a phosphate buffered saline washout, and the addition of mannitol or polyethylene glycol 35 kDa (PEG35). Perfusion outcomes were assessed using gross brain morphology, semi-quantitative grading of vascular blood clearance, quantitative detection of residual erythrocytes in whole-slide histological images, and light microscopic measures of cellular visualization and morphology. Perfusate osmolality was measured, and selected specimens were examined by electron microscopy. Results: Substantial vascular blood clearance was frequently achieved with fixative-only perfusion, indicating that a pre-fixation washout was not required to achieve high levels of blood clearance under the conditions tested. Routine light microscopy-based measures of cellular visibility and morphology were broadly similar across treatment groups. Adding mannitol and PEG35 to the fixative solution both led to concentration-dependent gross tissue shrinkage, particularly with PEG35, without correspondingly large or consistent changes on light microscopy. A substantial degree of case-to-case variation in perfusion quality was also observed among animals undergoing nominally similar procedures. Conclusions: Substantial variation in perfusate composition produced relatively modest differences in the light microscopy outcomes examined, despite pronounced effects of osmotic additives on gross brain morphology. There were some cases where perfusion with fixative alone led to near-complete blood vessel clearance, and the use of a preceding phosphate buffered saline washout was not associated with higher vascular clearance scores. The degree of case-to-case variability observed within treatment conditions suggests that procedural factors can play an important role on perfusion quality in addition to perfusate composition.

This tutorial seeks to facilitate the use of mobile electroencephalography (EEG) in young children. Mobile EEG allows researchers to investigate neural correlates of behavioral and cognitive processes in ecologically valid settings. While mobile EEG has been widely adopted in adult research, studies applying it to freely moving children remain scarce. However, investigating neural processes during active behavior and in interaction with movement is indispensable for advancing our understanding of neural correlates of cognitive development in early childhood. Here, we provide a practical tutorial on the collection and preprocessing of mobile EEG data from children. Drawing on experience and data from a large-scale study with toddlers, we summarize key methodological considerations, discuss common challenges and practical recommendations, and present a preprocessing pipeline developed for developmental mobile EEG data. In addition, we provide example EEG datasets from naturally moving 18-20-month-old toddlers. We argue that incorporating mobile EEG into developmental cognitive neuroscience allows researchers to (i) investigate cognitive development in naturalistic environments, (ii) examine associations among bodily movement, neural activity, and behavior, and (iii) study samples that are difficult to reach with stationary research. HighlightsO_LIMobile EEG enables investigation of neural processes during active behavior C_LIO_LIMobile EEG in moving freely children comes with unique methodological challenges C_LIO_LIWe provide practical recommendations for developmental mobile EEG studies C_LIO_LIA dataset and preprocessing pipeline from a toddler mobile EEG study are provided C_LI

Intracranial electroencephalography (iEEG) provides an unprecedented opportunity to directly record neural activity and causally perturb the human brain through electrical stimulation. Yet, the increasingly collaborative nature and complexity of modern iEEG studies pose substantial challenges for experimental control, data quality, and standardization. Unlike most experimental modalities, human iEEG data are acquired within dynamic clinical environments, where patient condition, recording quality, hardware configuration, and experimental protocols may vary across recording sessions and collaborating sites. The resulting heterogeneity creates opportunities for technical and procedural failures that often remain undetected until downstream analyses, when corrective action is no longer possible. Here, we present a framework for standardized session-level quality assurance in human iEEG research and provide an open-source implementation compatible with Brain Imaging Data Structure (BIDS)-organized datasets. The framework defines four complementary domains of quality assessment crucial for human iEEG studies: protocol fidelity, behavioral integrity, stimulation validation, and signal quality. These domains integrate electrophysiological recordings, behavioral event logs, and stimulation metadata to verify data completeness, confirm participant engagement, validate stimulation delivery, and identify potentially compromised recording channels. Automated quality metrics and standardized diagnostic visualizations are generated following each testing session, enabling rapid identification of technical and procedural failures while corrective action is still possible. By providing a standardized approach to session-level quality assurance, the framework improves data integrity, enhances reproducibility, facilitates analyst training, and supports harmonized data collection across laboratories and clinical sites.

BackgroundBrain regions integrate neural information arriving from several convergent projection sources. At the mesoscale level, neural projections can potentially span both hemispheres and extend along the entire rostrocaudal axis, which complicates efforts to map their full extent. To address this issue, we describe a novel method for mapping such mesoscale connectivity in vivo and ex vivo. Our neurotomographic approach utilizes micro-computed tomography (micro-CT) to image the spatial distribution of neural tracers bound to high Z-elements, e.g, gold. MethodsIn this study, we conjugated colloidal gold to a retrograde tracer wheat-germ agglutinin apo-horseradish peroxidase (WGA-HRP) and then stereotactically injected the gold-bound tracer (WAHG) into the mouse forebrain. Micro-CT was then used to image the brain in vivo and ex vivo, followed by three-dimensional reconstruction of tracer distribution. We then validated our approach by histologically processing the brains using silver enhancement to label gold particles; this enabled a direct comparison of histological labeling with the neurotomographic images. ResultsWe found that micro-CT imaging could reveal the major spatial distributions of the gold-bound tracer, which was consistent across in vivo and ex vivo imaging conditions. Moreover, the neurotomographically determined patterns corresponded with the labeling observed in histologically processed tissue, with the major sites of labeling reliably detected in reconstructed neurotomographic images. ConclusionsOverall, our findings demonstrate a potential novel method for non-destructive, three-dimensional mapping of neural tracers in vivo. This novel approach can potentially guide targeted multi-site recordings, enable validation of injection site placement, and facilitate rapid longitudinal connectomic analyses in vivo.

Suspension mass cytometry can measure 50 or more markers per cell and is not impacted by cellular autofluorescence, making it an appealing platform for comprehensive phenotyping of single cells in tissue with high innate fluorescence, such as brain. However, this organ is challenging to reduce to a single cell suspension: it is a deeply interconnected tissue rich in extracellular material and contains a wide variety of cell types including neurons, their supporting glia, and hematopoietic cells from peripheral circulation, any or all of which may have competing requirements for survival through the dissociation process. This study compares approaches for producing single-cell preparations from white and gray matter enriched regions of adult mouse brain, testing simple mechanical dissociation, Accutase, and three gentle enzymes plus DNase: papain, collagenase II, and collagenase plus dispase, all with or without Percoll gradient centrifugation to separate intact cells from debris. Live cells were counted by hemocytometer and Trypan blue exclusion to assess yield and compare across enzyme conditions. Mass cytometry was used to assess cellular composition after representative dissociation, debris removal, and viable cryopreservation. Key proteins measured included beta III-tubulin for neurons, glial fibrillary acidic protein (GFAP) for astrocytes, oligodendrocyte transcription factor (OLIG2) for oligodendrocytes, CD31 for endothelial cells, CD24 for ependymal cells, purinergic receptor P2Y12 (P2RY12) for microglia, and CD45 for leukocytes. Mechanical disaggregation paired with enzymatic dissociation by papain, Collagenase/Disapse, or Collagenase II plus DNase produced approximately 400,000 live cells per 100 grams of tissue. Cell yield was not significantly different between conditions and yields were similar between white and grey matter. Addition of Percoll gradient centrifugation substantially reduced the number of viable cells recovered (average decrease of 88%) and resulted in viable cell numbers that were insufficient for robust analysis of less common cell subsets. In addition to neurons, mass cytometry identified six major populations of non-neuronal cells: oligodendrocytes, astrocytes, endothelial cells, ependymal cells, microglia, and leukocytes. The relative abundance of these six populations was generally unchanged by cryopreservation in both grey matter (r = 0.95), and white matter (r = 0.90), though loss of oligodendrocytes did occur in both tissues. Proliferation, as measured by Ki67 positivity, was non-existent; consistent with this, levels of phosphorylated STAT1 (p-STAT1) and p-STAT3 proteins were low in all cell populations and unchanged by cryopreservation. A modest increase in p-S6(S235/236) after cryopreservation was consistently observed only in microglia. Together, these results establish standardized procedures for generating viable single cell suspensions for cytometry that preserve the cellular diversity of the adult murine brain with minimal perturbation of intracellular signaling cascades.

Behavioral annotation of synchronized video recordings is an essential step in developmental electroencephalography (EEG) research, supporting both the identification of behavior-related artifacts and the investigation of brain-behavior relationships. Existing annotation workflows, however, are often fragmented: proprietary EEG software provides limited flexibility for behavioral coding, whereas dedicated behavioral annotation platforms typically lack native integration with EEG data. We developed AVOCODO (Audio/VideO CODing Optimization), an open-source MATLAB-based software platform that integrates synchronized behavioral annotation directly into the EEG workflow. AVOCODO reads native EGI MFF recordings, synchronizes embedded video with EEG, visualizes the audio spectrogram to facilitate precise annotation of vocalizations, and writes user-defined behavioral events directly back into the original MFF recording as native EEG event markers while simultaneously exporting annotations as CSV files. The software supports fully customizable behavioral coding schemes, optional EEG visualization for quality control, and reloading of previously annotated recordings for review and inter-rater verification. Since its initial development in 2024, AVOCODO has been applied internally across five developmental EEG studies involving approximately 500 pediatric participants and more than 3,000 EEG recordings. By bridging behavioral annotation and EEG preprocessing within a unified open-source workflow, AVOCODO has the potential to improve the efficiency, reproducibility, and scalability of behavioral annotation in developmental EEG research.

From a statistical point of view, clustering spike time-windows from multi-electrode arrays (MEAs) recordings is a challenging high-dimensional, unsupervised clustering task where stationarity, commonly assumed in standard time-series analysis, is often violated and for which a gold standard is currently unavailable. Here we aim at providing practical guidance on how to cluster this type of data by systematically comparing 108 clustering pipelines differing along three main dimensions: (i) the data feature space; (ii) the metrics used to quantify distance between data points; and (iii) the clustering algorithm. For our benchmark we used both labeled synthetic data, mimicking four physiologically-inspired classes of spike time-windows, and a set of real MEAs recordings from a two-dimensional in-vitro neural culture. The performance of the competing pipelines was evaluated in terms of balanced accuracy and computational time when analyzing the synthetic datasets, while the Silhouette score was used for the real dataset, where no ground-truth is available. Overall, our analysis shows that the best combination is formed by k-means with Euclidean distance applied after Principal Component Analysis (PCA) of the spike time-windows. Conversely, hierarchical clustering showed the highest computational burden, while Independent Component Analysis and kernel PCA provided less effective noise suppression.

Independent component analysis (ICA) is widely used to remove artefacts from EEG and TMS-EEG recordings, but the components that correspond to artefacts are usually identified by manual inspection, which is subjective and difficult to reproduce. Existing automated classifiers are either designed for ordinary EEG and do not target the artefacts specific to concurrent transcranial magnetic stimulation, or are built on EEGLAB, so none is available for FieldTrip-based pipelines. We present TEFAR, a lightweight, configurable framework for semi-automatic artefact-component classification that applies the same detectors to ordinary EEG and TMS-EEG. TEFAR is built on FieldTrip and requires no additional MATLAB toolboxes. Each artefact class (line noise, blinks, lateral eye movements, cranial muscle, cardiac activity, and the TMS decay and recharge artefacts) is identified from the spectral, spatial, or temporal signature that its components are known to express. Detection thresholds use robust statistics (the median and the median absolute deviation rather than the mean and standard deviation), so that a single dominant artefact component cannot raise the threshold that is meant to detect it. In 30 ground-truth simulations, TEFAR reached a specificity of 1.000 and a sensitivity of 0.96-0.97 for both profiles. An application of TEFAR to real EEG and TMS-EEG recordings is the next step and will be reported separately.

With the rapid advancement of clinical neuroscience and Brain-Computer Interfaces (BCIs), there is an increasing demand for convenient, user-friendly EEG recording methods suitable for diverse environments, including mobile and home-based settings. Traditional gel-based EEG systems, while reliable, are inconvenient and time-consuming, whereas dry electrode systems tend to suffer from elevated noise levels. In this study, we investigated a novel methodological manipulation aimed at enhancing dry electrode signal quality: wetting the hair directly with tap water to improve scalp electrode conductivity. To this end, we recruited 22 healthy participants and compared their resting-state (RS) EEG activity across three experimental conditions (dry, semi-dry, and gel) within a single-session design. Specifically, we analyzed electrode impedance, spectral power (SP), and functional connectivity (FC). Our results show that basic hair-wetting substantially improved mean electrode impedance by 75.7% relative to the dry condition, reduced the proportion of bad channels from approximately 40% to 15%, and increased spectral-power similarity with gel recordings by 7.7% during eyes-closed recordings, and improved FC similarity by approximately 56-70% across frequency bands. Although this method does not fully match the signal quality of traditional gel-based systems, it represents a promising compromise, enhancing EEG data quality under suboptimal conditions. This approach offers a practical and non-invasive means to improve EEG signal quality, ultimately expanding real-world applications of EEG.

On-scalp MEG using optically pumped magnetometers (OPMs) have become more widespread with the recent introduction of the first whole-head systems that offer comparable spatial sampling to conventional MEG systems. For these systems to become truly useful in neuroscience and clinically they need to be able to deal with the kind of interference seen in typical lab and hospital environments - both environmental and participant related (e.g., dental wires). Conventional MEG uses sophisticated interference suppression algorithms to deal with this, but these tend to become instable at lower sensor counts - which makes up the majority of OPM-MEG systems to date. Alternative methods for OPM-MEG are needed. Several alternative algorithms have been proposed but a clear comparison between them is lacking. Here, we test and compare 5 of the most promising candidates, namely homogenous field correction (HFC), adaptive multipole models (AMM), signal space projection (SSP), signal space separation (SSS) and its iterative implementation (SSSit) using simulations and data recorded with a whole-head system. Recorded data includes a participant with dental wire - an especially difficult to deal with source of interference. We find that all algorithms tested achieve good interference suppression with dual-axis data. With single-axis data, SSS exhibited stability problems and several algorithms showed large signal loss (up to ~25%). SSP, temporal SSS and temporal AMM were able to suppress the dental artifact. Finally, we provide recommendations for researchers to determine help decide how to deal with interference in their OPM-MEG studies.

Wearable magnetoencephalography (MEG) enabled by optically pumped magnetometers (OPMs) promises improved comfort and motion tolerance. This is particularly beneficial when measuring brain activity in children who cannot sit still for long periods of time. Compared to cryogenic MEG, wearable MEG allows larger head movements, but they result in artifacts due to uncompensated background fields and reduce source localization accuracy. Spatial filtering methods can partially compensate these motion-induced artifacts, but they are most effective when used in combination with background field nulling. This is because accurate spatial filtering relies on an accurate estimate of the sensor gain and orientation of its sensitive axis. Through simulations, we first deduce the target residual background field that is necessary for accurate dipole localization (< 1 cm) in the presence of head movements. Using our open-source printed circuit board (PCB) coils, we develop a method to dynamically null the background field. We demonstrate that our dynamic field nulling method allows improved localization of somatosensory evoked fields (SEFs) by maintaining the background field below the target residual fields established in the simulations. Our study highlights the importance of tracking both the background field and the head position relative to the background field for quality assurance in wearable MEG.