Transient Artifacts Research Articles

Distortionless, free-breathing, and respiratory resolved 3D diffusion weighted imaging of the abdomen.

Abdominal imaging is frequently performed with breath holds or respiratory triggering to reduce the effects of respiratory motion. Diffusion weighted sequences provide a useful clinical contrast but have prolonged scan times due to low signal-to-noise ratio (SNR), and cannot be completed in a single breath hold. Echo-planar imaging (EPI) is the most commonly used trajectory for diffusion weighted imaging but it is susceptible to off-resonance artifacts. A respiratory resolved, three-dimensional (3D) diffusion prepared sequence that obtains distortionless diffusion weighted images during free-breathing is presented. Techniques to address the myriad of challenges including: 3D shot-to-shot phase correction, respiratory binning, diffusion encoding during free-breathing, and robustness to off-resonance are described. A twice-refocused, M1-nulled diffusion preparation was combined with an RF-spoiled gradient echo readout and respiratory resolved reconstruction to obtain free-breathing diffusion weighted images in the abdomen. Cartesian sampling permits a sampling density that enables 3D shot-to-shot phase navigation and reduction of transient fat artifacts. Theoretical properties of a region-based shot rejection are described. The region-based shot rejection method was evaluated with free-breathing (normal and exaggerated breathing), and respiratory triggering. The proposed sequence was compared in vivo with multishot DW-EPI. The proposed sequence exhibits no evident distortion in vivo when compared to multishot DW-EPI, robustness to B0 and B1 field inhomogeneities, and robustness to motion from different respiratory patterns. Acquisition of distortionless, diffusion weighted images is feasible during free-breathing with a b-value of 500 s/mm2, scan time of 6 min, and a clinically viable reconstruction time.

Reducing transient severe motion artifacts of gadoxetate disodium-enhanced MRI by oxygen inhalation: effective for pleural effusion but not ascites.

To evaluate the efficacy of low-flow oxygen inhalation in mitigating transient severe motion (TSM) artifacts associated with gadoxetate disodium-enhanced hepatic magnetic resonance imaging (MRI). Patients undergoing gadoxetate disodium-enhanced MRI were included. During the examination, the experimental group received oxygen at 2L/min via nasal cannula, while the control group did not. Images and TSM scores were evaluated and compared across precontrast, arterial, venous, and hepatobiliary phases. Subgroup analyses were conducted based on the presence of pleural effusion or ascites. A total of 325 patients were included. The motion scores were highest in the arterial phase and lowest in the hepatobiliary phase in both groups, but were significantly lower in the experimental group (p < 0.05). The incidence of TSM was significantly lower in the experimental group (3.29%) compared to the control group (13.29%, p = 0.01). While pleural effusion was associated with reduced image quality in both groups (p < 0.05), the image quality in the pleural effusion category was higher in the experimental group than in the control group. Oxygen inhalation showed limited efficacy in mitigating TSM related to ascites. Low-flow oxygen inhalation can effectively reduce the occurrence of gadoxetate disodium-related TSM. Pleural effusion may impair respiratory function and contribute to TSM, which can be alleviated by oxygen supplementation. However, Oxygen inhalation is less effective under the condition of ascites.

Improved circuitry and post-processing for interleaved fast-scan cyclic voltammetry and electrophysiology measurements.

The combination of electrophysiology and electrochemistry acquisition methods using a single carbon fiber microelectrode (CFM) in the brain has enabled more extensive analysis of neurochemical release, neural activity, and animal behavior. Predominantly, analog CMOS (Complementary Metal Oxide Semiconductor) switches are used for these interleaved applications to alternate the CFM output between electrophysiology and electrochemistry acquisition circuitry. However, one underlying issue with analog CMOS switches is the introduction of transient voltage artifacts in recorded electrophysiology signals resulting from CMOS charge injection. These injected artifacts attenuate electrophysiology data and delay reliable signal observation after every switch actuation from electrochemistry acquisition. Previously published attempts at interleaved electrophysiology and electrochemistry were able to recover reliable electrophysiology data within approximately 10-50 ms after switch actuation by employing various high-pass filtering methods to mitigate the observed voltage artifacts. However, high-pass filtering of this nature also attenuates valuable portions of the local-field potential (LFP) frequency range, thus limiting the extent of network-level insights that can be derived from in vivo measurements. This paper proposes a solution to overcome the limitation of charge injection artifacts that affect electrophysiological data while preserving important lower-frequency LFP bands. A voltage follower operational amplifier was integrated before the CMOS switch to increase current flow to the switch and dissipate any injected charge. This hardware addition resulted in a 16.98% decrease in electrophysiology acquisition delay compared to circuitry without a voltage follower. Additionally, single-term exponential modeling was implemented in post-processing to characterize and subtract remaining transient voltage artifacts in recorded electrophysiology data. As a result, electrophysiology data was reliably recovered 3.26 ± 0.22 ms after the beginning of the acquisition period (a 60% decrease from previous studies), while also minimizing LFP attenuation. Through these advancements, coupled electrophysiology and electrochemistry measurements can be conducted at higher scan rates while retaining data integrity for a more comprehensive analysis of neural activity and neurochemical release.

Denoising gravitational-wave signals from binary black holes with a dilated convolutional autoencoder

Abstract The broadband frequency output of gravitational-wave (GW) detectors is a non-stationary and non-Gaussian time series data stream dominated by noise populated by local disturbances and transient artifacts, which evolve on the same timescale as the GW signals and may corrupt the astrophysical information. We study a denoising algorithm dedicated to expose the astrophysical signals by employing a convolutional neural network in the encoder-decoder configuration, i.e. apply the denoising procedure of coalescing binary black hole signals to the publicly available LIGO O1 time series strain data. The denoising convolutional autoencoder neural network is trained on a dataset of simulated astrophysical signals injected into the real detector’s noise and a dataset of detector noise artifacts (‘glitches’), and its fidelity is tested on real GW events from O1 and O2 LIGO-Virgo observing runs.

Gaussian processes for glitch-robust gravitational-wave astronomy

ABSTRACT Interferometric gravitational-wave observatories have opened a new era in astronomy. The rich data produced by an international network enable detailed analysis of the curved space-time around black holes. With nearly 100 signals observed so far and thousands expected in the next decade, their population properties enable insights into stellar evolution and the expansion of our Universe. However, the detectors are afflicted by transient noise artefacts known as ‘glitches’ which contaminate the signals and bias inferences. Of the 90 signals detected to date, 18 were contaminated by glitches. This feasibility study explores a new approach to transient gravitational-wave data analysis using Gaussian processes, which model the underlying physics of the glitch-generating mechanism rather than the explicit realization of the glitch itself. We demonstrate that if the Gaussian process kernel function can adequately model the glitch morphology, we can recover the parameters of simulated signals. Moreover, we find that the Gaussian processes kernels used in this work are well suited to modelling long-duration glitches which are most challenging for existing glitch-mitigation approaches. Finally, we show how the time-domain nature of our approach enables a new class of time-domain tests of General Relativity, performing a re-analysis of the inspiral-merger-ringdown test on the first observed binary black hole merger. Our investigation demonstrates the feasibility of the Gaussian processes as an alternative to the traditional framework but does not yet establish them as a replacement. Therefore, we conclude with an outlook on the steps needed to realize the full potential of the Gaussian process approach.

Architectural optimization and feature learning for high-dimensional time series datasets

As our ability to sense increases, we are experiencing a transition from data-poor problems, in which the central issue is a lack of relevant data, to data-rich problems, in which the central issue is to identify a few relevant features in a sea of observations. Motivated by applications in gravitational-wave astrophysics, we study a problem in which the goal is to predict the presence of transient noise artifacts in a gravitational-wave detector from a rich collection of measurements from the detector and its environment. We argue that feature learning---in which relevant features are optimized from data---is critical to achieving high accuracy. We introduce models that reduce the error rate by over 60% compared to the previous state of the art, which used fixed, hand-crafted features. Feature learning is useful not only because it can improve performance on prediction tasks; the results provide valuable information about patterns associated with phenomena of interest that would otherwise be impossible to discover. In our motivating application, features found to be associated with transient noise provide diagnostic information about its origin and suggest mitigation strategies. Learning in such a high-dimensional setting is challenging. Through experiments with a variety of architectures, we identify two key factors in high-performing models: sparsity, for selecting relevant variables within the high-dimensional observations, and depth, which confers flexibility for handling complex interactions and robustness with respect to temporal variations. We illustrate their significance through a systematic series of experiments on real gravitational-wave detector data. Our results provide experimental corroboration of common assumptions in the machine-learning community and have direct applicability to improving our ability to sense gravitational waves, as well as to a wide variety of problem settings with similarly high-dimensional, noisy, or partly irrelevant data.

Robin's Viewer: Using deep-learning predictions to assist EEG annotation.

Machine learning techniques such as deep learning have been increasingly used to assist EEG annotation, by automating artifact recognition, sleep staging, and seizure detection. In lack of automation, the annotation process is prone to bias, even for trained annotators. On the other hand, completely automated processes do not offer the users the opportunity to inspect the models' output and re-evaluate potential false predictions. As a first step toward addressing these challenges, we developed Robin's Viewer (RV), a Python-based EEG viewer for annotating time-series EEG data. The key feature distinguishing RV from existing EEG viewers is the visualization of output predictions of deep-learning models trained to recognize patterns in EEG data. RV was developed on top of the plotting library Plotly, the app-building framework Dash, and the popular M/EEG analysis toolbox MNE. It is an open-source, platform-independent, interactive web application, which supports common EEG-file formats to facilitate easy integration with other EEG toolboxes. RV includes common features of other EEG viewers, e.g., a view-slider, tools for marking bad channels and transient artifacts, and customizable preprocessing. Altogether, RV is an EEG viewer that combines the predictive power of deep-learning models and the knowledge of scientists and clinicians to optimize EEG annotation. With the training of new deep-learning models, RV could be developed to detect clinical patterns other than artifacts, for example sleep stages and EEG abnormalities.

Generating transient noise artefacts in gravitational-wave detector data with generative adversarial networks

Transient noise glitches in gravitational-wave detector data limit the sensitivity of searches and contaminate detected signals. In this paper, we show how glitches can be simulated using generative adversarial networks (GANs). We produce hundreds of synthetic images for the 22 most common types of glitches seen in the LIGO, KAGRA, and Virgo detectors. We show how our GAN-generated images can easily be converted to time series, which would allow us to use GAN-generated glitches in simulations and mock data challenges to improve the robustness of gravitational-wave searches and parameter-estimation algorithms. We perform a neural network classification to show that our artificial glitches are an excellent match for real glitches, with an average classification accuracy across all 22 glitch types of 99.0%.

Comparison between 1.5-Tesla pseudocontinuous and pulsed arterial spin labeling in visualizing peri-ictal hyperperfusion: A case report

Arterial spin labeling (ASL) imaging with multiple postlabeling delays (PLD) can demonstrate the hemodynamics of peri-ictal hyperperfusion. Labeling methods include pulsed ASL (PASL) and pseudocontinuous ASL (pCASL). Although PASL is more widely used in daily clinical practice, it has several disadvantages because of lower delivery of labeled magnetization. We compared pCASL and PASL images acquired after a 24-h interval, using a 1.5-Tesla machine, in a 62-year-old man with non-convulsive status epilepticus. The pCASL during electrographic status epilepticus clearly demonstrated that ASL signals in the grey matter of the right hemisphere, particularly in the posteromedial part of the right temporal lobe, increased at PLDs of 1.5 s and 1.75 s and were almost washed out at 2.0 s. Although PASL, which was obtained during the ictal-interictal continuum when there were no changes in ictal findings on diffusion-weighted images, showed a similar trend, the increased signals were less pronounced than those of pCASL, with more contamination of arte-rial transit artifact in the subarachnoid space. Although the electroencephalographic findings were not the same when both ASLs were performed, the present study confirms that PASL is somewhat inferior to pCASL in detecting peri-ictal hyperperfusion because of the greater effect of arterial transient artifacts.

Exposing Collections through Interaction Ecologies: A Prototype for Architectural Ephemera

This study introduces a model for the observation and design of digital collections exhibitions rooted on three concepts: interaction ecologies, exposing collections, and architectural ephemera. The study examines how to go beyond the “one user, one device” approach in GLAMs, what it means to expose a collection, and what it means to consider architectural elements as ephemera. We define interaction ecologies as the approach to a context in which individuals of different species (devices and humans included) entertain rich and floating relationships appropriate to the roles, activities, and postures they assume. This perspective offers novel possibilities on the exhibition of digital collections. In addition, we discuss the concepts of exposing a collection and the translation of architectural elements to transient artifacts to preserve memory. This discussion allows a series of qualities to emerge that together constitute a model for the observation of collection exhibitions. The study includes the presentation of the exhibition “Ankara Balcony Balustrades 1950–75,” which, by focusing on these aspects, acts as a prototype of the model and helps in verifying the application and its results.

A Method for Optimizing the Artifact Subspace Reconstruction Performance in Low-Density EEG

Electroencephalogram (EEG) plays a significant role in the analysis of cerebral activity, although the recorded electrical brain signals are always contaminated with artifacts. This represents the major issue limiting the use of the EEG in daily life applications, as the artifact removal process still remains a challenging task. Among the available methodologies, artifact subspace reconstruction (ASR) is a promising tool that can effectively remove transient or large-amplitude artifacts. However, the effectiveness of ASR and the optimal choice of its parameters have been validated only for high-density EEG acquisitions. In this regard, this study proposes an enhanced procedure for the optimal individuation of ASR parameters, in order to successfully remove artifacts in low-density EEG acquisitions (down to four channels). The proposed method starts from the analysis of real EEG data, to generate a large semisimulated dataset with similar characteristics. Through a fine-tuning procedure on this semisimulated data, the proposed method identifies the optimal parameters to be used for artifact removal on real data. The results show that the algorithm achieves an efficient removal of artifacts preserving brain signal information, also in low-density EEG signals, thus favoring the adoption of the EEG also for more portable and/or daily-life applications.

The WQN algorithm to adaptively correct artifacts in the EEG signal

Wavelet quantile normalization (WQN) is a nonparametric algorithm designed to efficiently remove transient artifacts from single-channel EEG in real-time clinical monitoring. Today, EEG monitoring machines suspend their output when artifacts in the signal are detected. Removing unpredictable EEG artifacts would thus allow to improve the continuity of the monitoring. We analyze the WQN algorithm which consists in transporting wavelet coefficient distributions of an artifacted epoch into a reference, uncontaminated signal distribution. We show that the algorithm regularizes the signal. To confirm that the algorithm is well suited, we study the empirical distributions of the EEG and the artifacts wavelet coefficients. We compare the WQN algorithm to the classical wavelet thresholding methods and study their effect on the distribution of the wavelet coefficients. We show that the WQN algorithm preserves the distribution while the thresholding methods can cause alterations. Finally, we show how the spectrogram computed from an EEG signal can be cleaned using the WQN algorithm.

Parameterised population models of transient non-Gaussian noise in the LIGO gravitational-wave detectors

The two interferometric LIGO gravitational-wave observatories provide the most sensitive data to date to study the gravitational-wave universe. As part of a global network, they have completed their third observing run in which they observed many tens of signals from merging compact binary systems. It has long been known that a limiting factor in identifying transient gravitational-wave signals is the presence of transient non-Gaussian noise, which reduce the ability of astrophysical searches to detect signals confidently. Significant efforts are taken to identify and mitigate this noise at the source, but its presence persists, leading to the need for software solutions. Taking a set of transient noise artefacts categorised by the GravitySpy software during the O3a observing era, we produce parameterised population models of the noise projected into the space of astrophysical model parameters of merging binary systems. We compare the inferred population properties of transient noise artefacts with observed astrophysical systems from the GWTC2.1 catalogue. We find that while the population of astrophysical systems tend to have near equal masses and moderate spins, transient noise artefacts are typically characterised by extreme mass ratios and large spins. This work provides a new method to calculate the consistency of an observed candidate with a given class of noise artefacts. This approach could be used in assessing the consistency of candidates found by astrophysical searches (i.e. determining if they are consistent with a known glitch class). Furthermore, the approach could be incorporated into astrophysical searches directly, potentially improving the reach of the detectors, though only a detailed study would verify this.

Simulating transient noise bursts in LIGO with generative adversarial networks

The noise of gravitational-wave (GW) interferometers limits their sensitivity and impacts the data quality, hindering the detection of GW signals from astrophysical sources. For transient searches, the most problematic are transient noise artifacts, known as glitches, that happen at a rate around $1\text{ }\text{ }{\mathrm{min}}^{\ensuremath{-}1}$, and can mimic GW signals. Because of this, there is a need for better modeling and inclusion of glitches in large-scale studies, such as stress testing the pipelines. In this proof-of concept work we employ generative adversarial networks (GAN), a state-of-the-art deep learning algorithm inspired by game theory, to learn the underlying distribution of blip glitches and to generate artificial populations. We reconstruct the glitch in the time domain, providing a smooth input that the GAN can learn. With this methodology, we can create distributions of $\ensuremath{\sim}{10}^{3}$ glitches from Hanford and Livingston detectors in less than 1 sec. Furthermore, we employ several metrics to measure the performance of our methodology and the quality of its generations. This investigation will be extended in the future to different glitch classes with the final goal of creating an open-source interface for mock data generation.

Fully automatic quantification of transient severe respiratory motion artifact of gadoxetate disodium-enhanced MRI during arterial phase.

It is important to fully automate the evaluation of gadoxetate disodium-enhanced arterial phase images because the efficient quantification of transient severe motion artifacts can be used in a variety of applications. Our study proposes a fully automatic evaluation method of motion artifacts during the arterial phase of gadoxetate disodium-enhanced MR imaging. The proposed method was based on the construction of quality-aware features to represent the motion artifact using MR image statistics and multidirectional filtered coefficients. Using the quality-aware features, the method calculated quantitative quality scores of gadoxetate disodium-enhanced images fully automatically. The performance of our proposed method, as well as two other methods, was acquired by correlating scores against subjective scores from radiologists based on the 5-point scale and binary evaluation. The subjective scores evaluated by two radiologists were severity scores of motion artifacts in the evaluation set on a scale of 1 (no motion artifacts) to 5 (severe motion artifacts). Pearson's linear correlation coefficient (PLCC) and Spearman's rank-ordered correlation coefficient (SROCC) values of our proposed method against the subjective scores were 0.9036 and 0.9057, respectively, whereas the PLCC values of two other methods were 0.6525 and 0.8243, and the SROCC values were 0.6070 and 0.8348. Also, in terms of binary quantification of transient severe respiratory motion, the proposed method achieved 0.9310 sensitivity, 0.9048 specificity, and 0.9200 accuracy, whereas the other two methods achieved 0.7586, 0.8996 sensitivities, 0.8098, 0.8905 specificities, and 0.9200, 0.9048 accuracies CONCLUSIONS: This study demonstrated the high performance of the proposed automatic quantification method in evaluating transient severe motion artifacts in arterial phase images.

Transient severe motion artifacts on gadoxetic acid-enhanced MRI: risk factor analysis in 2230 patients.

To determine risk factors for transient severe motion (TSM) artifact on arterial phase of gadoxetic acid-enhanced MRI using a large cohort. A total of 2230 patients who underwent gadoxetic acid-enhanced MRI was consecutively included. Two readers evaluated respiratory motion artifact on arterial phase images using a 5-point grading scale. Clinical factors including demographic data, underlying disease, laboratory data, presence of ascites and pleural effusion, and previous experience of gadoxetic acid-enhanced MRI were investigated. Univariable and multivariable logistic regression analyses were performed to determine significant risk factors for TSM. Predictive value of TSM was calculated according to the number of significant risk factors. Overall incidence of TSM was 5.0% (111/2230). In the multivariable analysis, old age (≥ 65 years; odds ratio [OR] = 2.01 [95% CI, 1.31-3.07]), high body mass index (≥ 25 kg/m2; OR = 1.76 [1.18-2.63]), chronic obstructive pulmonary disease (OR = 6.11 [2.32-16.04]), and moderate to severe pleural effusion (OR = 3.55 [1.65-7.65]) were independent significant risk factors for TSM. Presence of hepatitis B (OR = 0.66 [0.43-0.99]) and previous experience of gadoxetic acid-enhanced MRI (OR = 0.52 [0.33-0.83]) were negative risk factors for TSM. When at least one of the significant factors was present, the predictive risk was 5.7% (109/1916), whereas it was 16.3% (17/104) when at least four factors were present. Knowing risk factors for transient severe motion artifact on gadoxetic acid-enhanced MRI can be clinically useful for providing diagnostic strategies more tailored to individual patients. • Old age, high body mass index, chronic obstructive pulmonary disease, and moderate to severe pleural effusion were independent risk factors for transient severe motion artifact on gadoxetic acid-enhanced MRI. • Patients with hepatitis B or previous experience of gadoxetic acid-enhanced MRI were less likely to show transient severe motion artifact. • As the number of risk factors for transient severe motion artifact increased, the predicted risk for it also showed a tendency to increase.

Concurrent stimulation and sensing in bi-directional brain interfaces: a multi-site translational experience

Objective. To provide a design analysis and guidance framework for the implementation of concurrent stimulation and sensing during adaptive deep brain stimulation (aDBS) with particular emphasis on artifact mitigations. Approach. We defined a general architecture of feedback-enabled devices, identified key components in the signal chain which might result in unwanted artifacts and proposed methods that might ultimately enable improved aDBS therapies. We gathered data from research subjects chronically-implanted with an investigational aDBS system, Summit RC + S, to characterize and explore artifact mitigations arising from concurrent stimulation and sensing. We then used a prototype investigational implantable device, DyNeuMo, and a bench-setup that accounts for tissue–electrode properties, to confirm our observations and verify mitigations. The strategies to reduce transient stimulation artifacts and improve performance during aDBS were confirmed in a chronic implant using updated configuration settings. Main results. We derived and validated a ‘checklist’ of configuration settings to improve system performance and areas for future device improvement. Key considerations for the configuration include (a) active instead of passive recharge, (b) sense-channel blanking in the amplifier, (c) high-pass filter settings, (d) tissue–electrode impedance mismatch management, (e) time-frequency trade-offs in the classifier, (f) algorithm blanking and transition rate limits. Without proper channel configuration, the aDBS algorithm was susceptible to limit-cycles of oscillating stimulation independent of physiological state. By applying the checklist, we could optimize each block’s performance characteristics within the overall system. With system-level optimization, a ‘fast’ aDBS prototype algorithm was demonstrated to be feasible without reentrant loops, and with noise performance suitable for subcortical brain circuits. Significance. We present a framework to study sources and propose mitigations of artifacts in devices that provide chronic aDBS. This work highlights the trade-offs in performance as novel sensing devices translate to the clinic. Finding the appropriate balance of constraints is imperative for successful translation of aDBS therapies. Clinical trial: Institutional Review Board and Investigational Device Exemption numbers: NCT02649166/IRB201501021 (University of Florida), NCT04043403/IRB52548 (Stanford University), NCT03582891/IRB1824454 (University of California San Francisco). IDE #180 097.

Influence of age on gadoxetic acid disodium-induced transient respiratory motion artifacts in pediatric liver MRI.

Gd-EOB-DTPA-enhanced liver MRI is frequently compromised by transient severe motion artifacts (TSM) in the arterial phase, which limits image interpretation for the detection and differentiation of focal liver lesions and for the recognition of the arterial vasculature before and after liver transplantation. The purpose of this study was to investigate which patient factors affect TSM in children who undergo Gd-EOB-DTPA-enhanced liver MRI and whether younger children are affected as much as adolescents. One hundred and forty-eight patients (65 female, 83 male, 0.1-18.9 years old), who underwent 226 Gd-EOB-DTPA-enhanced MRIs were included retrospectively in this single-center study. The occurrence of TSM was assessed by three readers using a four-point Likert scale. The relation to age, gender, body mass index, indication for MRI, requirement for sedation, and MR repetition was investigated using uni- and multivariate logistic regression analysis. In Gd-EOB-DTPA-enhanced MRIs, TSM occurred in 24 examinations (10.6%). Patients with TSM were significantly older than patients without TSM (median 14.3 years; range 10.1-18.1 vs. 12.4 years; range 0.1-18.9, p<0.001). TSM never appeared under sedation. Thirty of 50 scans in patients younger than 10 years were without sedation. TSM were not observed in non-sedated patients younger than 10 years of age (p = 0.028). In a logistic regression analysis, age remained the only cofactor independently associated with the occurrence of TSM (hazard ratio 9.152, p = 0.049). TSM in Gd-EOB-DTPA-enhanced liver MRI do not appear in children under the age of 10 years.

Ludic Heritage: Supplementary School Plays, Museum Collaborations, and the Futurity of Histories in Migration

This article introduces the term "ludic heritage" to describe how people relocate histories in migration through play. In a collaboration between the Museum of London, an Albanian supplementary school, and the researcher, play became a point of intersection between adults' and children's worlds, the museum and minority communities, and imagination, memory, and reality. UK supplementary schools set up by migrant communities teach children their cultural heritages not taught in mainstream schools or represented at the museum. Despite an estimation of 3,000-5,000 supplementary schools in London alone, they exist on the periphery of public life. In school plays performed in museum festivals children embodied the past whilst improvising cultural representations on stage, redirecting the future of heritage in migration. Framing time as technique (Bear 2016) and as an embodied mental capacity (Da Silva Sinha 2019), I observe how people construct the temporalities they inhabit through a praxis (Munn 1992) in which play—as an adaptable and malleable form of cultural heritage—reconfigures cultural identities and mobilizes a new community in diaspora. I draw attention to how different partners approached play, as research method, embodied memory, pedagogy, or museum engagement or through transient cultural artifacts that open temporal possibilities.

Automated Pipeline for Infants Continuous EEG (APICE): A flexible pipeline for developmental cognitive studies

Infant electroencephalography (EEG) presents several challenges compared with adult data: recordings are typically short and heavily contaminated by motion artifacts, and the signal changes throughout development. Traditional data preprocessing pipelines, developed mainly for event-related potential analyses, require manual steps. However, larger datasets make this strategy infeasible. Moreover, new analytical approaches may have different preprocessing requirements. We propose an Automated Pipeline for Infants Continuous EEG (APICE). APICE is fully automated, flexible, and modular. The use of multiple algorithms and adaptive thresholds for artifact detection makes it suitable across age groups and testing procedures. Furthermore, the preprocessing is performed on continuous data, enabling better data recovery and flexibility (i.e., the same preprocessing is usable for different analyzes). Here we describe APICE and validate its performance in terms of data quality and data recovery using two very different infant datasets. Specifically, (1) we show how APICE performs when varying its artifacts rejection sensitivity; (2) we test the effect of different data cleaning methods such as the correction of transient artifacts, Independent Component Analysis, and Denoising Source Separation; and (3) we compare APICE with other available pipelines. APICE uses EEGLAB and compatible custom functions. It is freely available at https://github.com/neurokidslab/eeg_preprocessing, together with example scripts.