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Developmental EEG: Advances on Data Analysis Methods
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Developmental EEG: Advances on Data Analysis Methods

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Wearables".

Deadline for manuscript submissions: closed (31 July 2024) | Viewed by 7013

Special Issue Editor

Dr. Marco Buiatti

E-Mail Website
Guest Editor
Center for Mind/Brain Sciences (CIMeC), University of Trento, 38068 Rovereto, Italy
Interests: EEG/MEG data analysis; developmental EEG artifact correction; frequency-tagging analysis; scale-free analysis; wearable wireless EEG for infants; developmental cognitive neuroscience

Special Issue Information

Dear Colleagues,

Electroencephalography (EEG) is a valuable methodology for investigating human brain development from birth because it provides a direct measure of brain activity, it is relatively inexpensive and it is easy to use on subjects of any age, starting from the very first days of life. EEG applications on developmental populations span from clinical monitoring and diagnosis at the bedside to basic research on the neural predispositions and development of core perceptual, cognitive and motor functions. However, measuring reliable brain responses in infants is challenging because of widespread non-stereotypical artifacts and (in case of stimulus-related paradigms) reduced data statistics due to their very limited attentional span.

The aim of this Special Issue is to promote cutting-edge innovative methods focused on the challenges of developmental EEG data analysis. To foster reliability and reproducibility in this emerging field, we encourage authors to explicitly highlight three key aspects of their proposed methods: 1) the novelty and added value of their method compared to the state of the art; 2) generalization: the method should be validated on an independent dataset; 3) performance: validation should include a quantitative comparison with state-of-the-art methods tackling the same problem. Public sharing of code for the community is highly appreciated. Reviews and well-motivated opinion papers are also welcome.

Topics of interest include (but are not limited to) the following:

  • Artifact detection and correction/removal in developmental EEG;
  • Resting-state measures in developmental EEG;
  • Clinical assessment of pediatric EEG;
  • Innovative methods or improvements of existing methods for developmental EEG data analysis in stimulus-related paradigms (event-related potentials, time-frequency analysis, frequency-tagging, etc.);
  • Source localization and functional connectivity analysis of developmental EEG;
  • EEG-based biomarkers of neurodevelopmental disorders.

Dr. Marco Buiatti
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (3 papers)

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Research

17 pages, 4027 KiB  
Article
EEG Functional Connectivity Analysis for the Study of the Brain Maturation in the First Year of Life
by Anna Falivene, Chiara Cantiani, Chiara Dondena, Elena Maria Riboldi, Valentina Riva and Caterina Piazza
Sensors 2024, 24(15), 4979; https://doi.org/10.3390/s24154979 - 1 Aug 2024
Cited by 1 | Viewed by 1877
Abstract
Brain networks are hypothesized to undergo significant changes over development, particularly during infancy. Thus, the aim of this study is to evaluate brain maturation in the first year of life in terms of electrophysiological (EEG) functional connectivity (FC). Whole-brain FC metrics (i.e., magnitude-squared [...] Read more.
Brain networks are hypothesized to undergo significant changes over development, particularly during infancy. Thus, the aim of this study is to evaluate brain maturation in the first year of life in terms of electrophysiological (EEG) functional connectivity (FC). Whole-brain FC metrics (i.e., magnitude-squared coherence, phase lag index, and parameters derived from graph theory) were extracted, for multiple frequency bands, from baseline EEG data recorded from 146 typically developing infants at 6 (T6) and 12 (T12) months of age. Generalized linear mixed models were used to test for significant differences in the computed metrics considering time point and sex as fixed effects. Correlational analyses were performed to ascertain the potential relationship between FC and subjects’ cognitive and language level, assessed with the Bayley-III scale at 24 (T24) months of age. The results obtained highlighted an increased FC, for all the analyzed frequency bands, at T12 with respect to T6. Correlational analyses yielded evidence of the relationship between FC metrics at T12 and cognition. Despite some limitations, our study represents one of the first attempts to evaluate brain network evolution during the first year of life while accounting for correspondence between functional maturation and cognitive improvement. Full article
(This article belongs to the Special Issue Developmental EEG: Advances on Data Analysis Methods)
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13 pages, 3616 KiB  
Article
A Within-Subject Multimodal NIRS-EEG Classifier for Infant Data
by Jessica Gemignani and Judit Gervain
Sensors 2024, 24(13), 4161; https://doi.org/10.3390/s24134161 - 26 Jun 2024
Cited by 1 | Viewed by 2025
Abstract
Functional Near Infrared Spectroscopy (fNIRS) and Electroencephalography (EEG) are commonly employed neuroimaging methods in developmental neuroscience. Since they offer complementary strengths and their simultaneous recording is relatively easy, combining them is highly desirable. However, to date, very few infant studies have been conducted [...] Read more.
Functional Near Infrared Spectroscopy (fNIRS) and Electroencephalography (EEG) are commonly employed neuroimaging methods in developmental neuroscience. Since they offer complementary strengths and their simultaneous recording is relatively easy, combining them is highly desirable. However, to date, very few infant studies have been conducted with NIRS-EEG, partly because analyzing and interpreting multimodal data is challenging. In this work, we propose a framework to carry out a multivariate pattern analysis that uses an NIRS-EEG feature matrix, obtained by selecting EEG trials presented within larger NIRS blocks, and combining the corresponding features. Importantly, this classifier is intended to be sensitive enough to apply to individual-level, and not group-level data. We tested the classifier on NIRS-EEG data acquired from five newborn infants who were listening to human speech and monkey vocalizations. We evaluated how accurately the model classified stimuli when applied to EEG data alone, NIRS data alone, or combined NIRS-EEG data. For three out of five infants, the classifier achieved high and statistically significant accuracy when using features from the NIRS data alone, but even higher accuracy when using combined EEG and NIRS data, particularly from both hemoglobin components. For the other two infants, accuracies were lower overall, but for one of them the highest accuracy was still achieved when using combined EEG and NIRS data with both hemoglobin components. We discuss how classification based on joint NIRS-EEG data could be modified to fit the needs of different experimental paradigms and needs. Full article
(This article belongs to the Special Issue Developmental EEG: Advances on Data Analysis Methods)
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21 pages, 12262 KiB  
Article
Repairing Artifacts in Neural Activity Recordings Using Low-Rank Matrix Estimation
by Shruti Naik, Ghislaine Dehaene-Lambertz and Demian Battaglia
Sensors 2023, 23(10), 4847; https://doi.org/10.3390/s23104847 - 17 May 2023
Viewed by 2185
Abstract
Electrophysiology recordings are frequently affected by artifacts (e.g., subject motion or eye movements), which reduces the number of available trials and affects the statistical power. When artifacts are unavoidable and data are scarce, signal reconstruction algorithms that allow for the retention of sufficient [...] Read more.
Electrophysiology recordings are frequently affected by artifacts (e.g., subject motion or eye movements), which reduces the number of available trials and affects the statistical power. When artifacts are unavoidable and data are scarce, signal reconstruction algorithms that allow for the retention of sufficient trials become crucial. Here, we present one such algorithm that makes use of large spatiotemporal correlations in neural signals and solves the low-rank matrix completion problem, to fix artifactual entries. The method uses a gradient descent algorithm in lower dimensions to learn the missing entries and provide faithful reconstruction of signals. We carried out numerical simulations to benchmark the method and estimate optimal hyperparameters for actual EEG data. The fidelity of reconstruction was assessed by detecting event-related potentials (ERP) from a highly artifacted EEG time series from human infants. The proposed method significantly improved the standardized error of the mean in ERP group analysis and a between-trial variability analysis compared to a state-of-the-art interpolation technique. This improvement increased the statistical power and revealed significant effects that would have been deemed insignificant without reconstruction. The method can be applied to any time-continuous neural signal where artifacts are sparse and spread out across epochs and channels, increasing data retention and statistical power. Full article
(This article belongs to the Special Issue Developmental EEG: Advances on Data Analysis Methods)
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