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Developmental research using electroencephalography (EEG) offers valuable insights in brain processes early in life, but at the same time, applying this sensitive technique to young children who are often non-compliant and have short attention spans comes with practical limitations. It is thus of particular importance to optimally use the limited resources to advance our understanding of development through reproducible and replicable research practices. Here, we describe methodological approaches that help maximize the reproducibility of developmental EEG research. We discuss how to transform EEG data into the standardized Brain Imaging Data Structure (BIDS) which organizes data according to the FAIR data sharing principles. We provide a tutorial on how to use cluster-based permutation testing to analyze developmental EEG data. This versatile test statistic solves the multiple comparison problem omnipresent in EEG analysis and thereby substantially decreases the risk of reporting false discoveries. Finally, we describe how to quantify effect sizes, in particular of cluster-based permutation results. Reporting effect sizes conveys a finding's impact and robustness which in turn informs future research. To demonstrate these methodological approaches to data organization, analysis and report, we use a publicly accessible infant EEG dataset and provide a complete copy of the analysis code. • Methods for enhancing reproducibility in developmental EEG research. • Tutorial for converting EEG data into BIDS to adopt FAIR data sharing principles. • How to use cluster-based permutation testing to analyze developmental EEG data. • How to quantify effect sizes, particularly of cluster-based permutation results. [ABSTRACT FROM AUTHOR]

Abstract: Language is often perceived together with visual information. This raises the question on how the brain integrates information conveyed in visual and/or linguistic format during spoken language comprehension. In this study we investigated the dynamics of semantic integration of visual and linguistic information by means of time-frequency analysis of the EEG signal. A modified version of the N400 paradigm with either a word or a picture of an object being semantically incongruous with respect to the preceding sentence context was employed. Event-Related Potential (ERP) analysis showed qualitatively similar N400 effects for integration of either word or picture. Time-frequency analysis revealed early specific decreases in alpha and gamma band power for linguistic and visual information respectively. We argue that these reflect a rapid context-based analysis of acoustic (word) or visual (picture) form information. We conclude that although full semantic integration of linguistic and visual information occurs through a common mechanism, early differences in oscillations in specific frequency bands reflect the format of the incoming information and, importantly, an early context-based detection of its congruity with respect to the preceding language context. [Copyright &y& Elsevier]

Abstract: In this paper, we show how ElectroEncephaloGraphic (EEG) and MagnetoEncephaloGraphic (MEG) data can be analyzed statistically using nonparametric techniques. Nonparametric statistical tests offer complete freedom to the user with respect to the test statistic by means of which the experimental conditions are compared. This freedom provides a straightforward way to solve the multiple comparisons problem (MCP) and it allows to incorporate biophysically motivated constraints in the test statistic, which may drastically increase the sensitivity of the statistical test. The paper is written for two audiences: (1) empirical neuroscientists looking for the most appropriate data analysis method, and (2) methodologists interested in the theoretical concepts behind nonparametric statistical tests. For the empirical neuroscientist, a large part of the paper is written in a tutorial-like fashion, enabling neuroscientists to construct their own statistical test, maximizing the sensitivity to the expected effect. And for the methodologist, it is explained why the nonparametric test is formally correct. This means that we formulate a null hypothesis (identical probability distribution in the different experimental conditions) and show that the nonparametric test controls the false alarm rate under this null hypothesis. [Copyright &y& Elsevier]

• We tested a 3D scanner as an affordable and fast electrode digitizer. • The accuracy of the 3D scanned electrode positions is higher than template positions. • EEG source models improve with 3D scanned electrode positions. In this study, we evaluated the use of a structured-light 3D scanner for EEG electrode digitization. We tested its accuracy, robustness and evaluated its practical feasibility. Furthermore, we assessed how 3D scanning of EEG electrode positions affects the accuracy of EEG volume conduction models and source localization. To assess the improvement in electrode positions and source results, we compared the electrode positions both at the scalp level and by quantifying source model accuracy between the 3D scanner, generic template, and cap-specific electrode positions. The use of the 3D scanner significantly improves the accuracy of EEG electrode positions to a median error of 9.4 mm and maximal error of 32.8 mm, relative to the custom (median error of 10.9 mm, maximal error 39.1 mm) and manufacturer's template positions (median error of 13.8 mm, maximal error 57.0 mm). The relative difference measure (RDM) of the EEG source model averaged over the brain improves from 0.18 to 0.11. The dipole localization error averaged over the brain improves from 11.4 mm to 7.0 mm. A structured-light 3D scanner improves the electrode position accuracy and thereby the EEG source model accuracy. It is more affordable than systems currently used for this, and allows for robust and fast digitization. Therefore, we consider it a cost and time-efficient way to improve EEG source reconstruction. [ABSTRACT FROM AUTHOR]

Abstract: We used simultaneously recorded EEG and fMRI to investigate in which areas the BOLD signal correlates with frontal theta power changes, while subjects were quietly lying resting in the scanner with their eyes open. To obtain a reliable estimate of frontal theta power we applied ICA on band-pass filtered (2–9 Hz) EEG data. For each subject we selected the component that best matched the mid-frontal scalp topography associated with the frontal theta rhythm. We applied a time-frequency analysis on this component and used the time course of the frequency bin with the highest overall power to form a regressor that modeled spontaneous fluctuations in frontal theta power. No significant positive BOLD correlations with this regressor were observed. Extensive negative correlations were observed in the areas that together form the default mode network. We conclude that frontal theta activity can be seen as an EEG index of default mode network activity. [Copyright &y& Elsevier]