Challenges and new perspectives of developmental cognitive EEG studies

Hervé, Estelle; Mento, Giovanni; Clément, François

doi:10.1016/j.neuroimage.2022.119508

Cited by 17 publications

(8 citation statements)

References 260 publications

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“…This information could be useful to automotive industry personnel, allowing them to focus more on cognitive aspects while designing and manufacturing automobiles. Additionally, future research is urged to measure and examine EEG signals to investigate the brain's overall electrical activity and evaluate cognitive function during the exposure to vibrating (Hervé et al, 2022; Wen et al, 2022). Future studies should measure visual acuity, hand–eye coordination, response time, and memory under the WBV conditions. Although this study focused on a sample of nine participants, future research could expand the sample size to improve the generalizability of the findings.…”

Section: Conclusion and Future Implicationmentioning

confidence: 99%

Section: Conclusion and Future Implicationmentioning

confidence: 99%

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Real‐time vibration monitoring and analysis of agricultural tractor drivers using an IoT‐based system

Singh

Nawayseh

Samuel

et al. 2023

Journal of Field Robotics

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Agricultural tractor drivers experience a high amplitude of vibration, especially during soil tillage operations. In the past, most research studied vibration exposure with more focus on the vertical (z) axis than on the fore‐and‐aft (x) and lateral (y) axes. This study examines how rotary soil tillage affects the vibration acceleration and frequency, and the power spectral densities (PSDs) at the seat pan and head along three translational axes in a real‐field multiaxis vibration context. Moreover, this study aimed to identify the characteristics of the seat‐to‐head transmissibility (STHT) response to identifying the most salient resonant frequencies along the x‐, y‐, and z‐axes. Nine (9) male tractor drivers operated the tractor with a mounted rotary tiller throughout the soil tillage process. In the event of a COVID‐19 pandemic, and to respect social distancing, this study developed an Internet of Things (IoT) module with the potential to integrate with existing data loggers for online data transmission and to make the experimentation process more effective by removing potential sources of experimenter errors. The raw acceleration data retrieved at the seat pan and the head were utilized to obtain daily exposure (A(8)), PSDs, and STHT along the x‐, y‐, and z‐axes. The vibration energy was found to be dominant along the z‐axis than the x‐ and y‐axes. A(8) response among tractor drivers exceeds the exposure action value explicitly stated by Directive 2002/44/EU. PSDs along the x‐, y‐, and z‐axes depicted the low‐frequency vibration induced by rotary soil tillage operation. The STHT response exhibited a higher degree of transmissibility along the y‐ and z‐axes when compared with that along the x‐axis. The frequency range of 4–7 Hz may plausibly be associated with cognitive impairment in tractor drivers during rotary soil tillage.

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Section: Conclusion and Future Implicationmentioning

confidence: 99%

Section: Conclusion and Future Implicationmentioning

confidence: 99%

Real‐time vibration monitoring and analysis of agricultural tractor drivers using an IoT‐based system

Singh

Nawayseh

Samuel

et al. 2023

Journal of Field Robotics

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“…EEG is often also used to assess neural activity across developmental populations (both typically developing and clinical), which can pose additional challenges for data cleaning. For instance, movement and muscle-related artifacts are often more apparent in children (Herve et al, 2022), while their limited attentional capabilities often necessitate shorter recording times and less cognitively demanding tasks (Bell & Cuevas, 2012; DiStefano et al, 2019; Herve et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

RELAX-Jr: An Automated Pre-Processing Pipeline for Developmental EEG Recordings

Hill,

Enticott,

Fitzgerald

et al. 2024

Preprint

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Automated EEG pre-processing pipelines provide several key advantages over traditional manual data cleaning approaches; primarily, they are less time-intensive and remove potential experimenter error/bias. Automated pipelines also require fewer technical expertise as they remove the need for manual artifact identification. We recently developed the fully automated Reduction of Electroencephalographic Artifacts (RELAX) pipeline and demonstrated its performance in cleaning EEG data recorded from adult populations. Here, we introduce the RELAX-Jr pipeline, which was adapted from RELAX to be designed specifically for pre- processing of data collected from children. RELAX-Jr implements multi-channel Wiener filtering (MWF) and/or wavelet-enhanced independent component analysis (wICA) combined with the adjusted-ADJUST automated independent component classification algorithm to identify and reduce all artifacts using algorithms adapted to optimally identify artifacts in EEG recordings taken from children. Using a dataset of resting-state EEG recordings (N = 136) from children spanning early-to-middle childhood (4-12 years), we assessed the cleaning performance of RELAX-Jr using a range of metrics including signal-to-error ratio, artifact-to-residue ratio, ability to reduce blink and muscle contamination, and differences in estimates of alpha power between eyes-open and eyes-closed recordings. We also compared the performance of RELAX- Jr against four publicly available automated cleaning pipelines. We demonstrate that RELAX-Jr provides strong cleaning performance across a range of metrics, supporting its use as an effective and fully automated cleaning pipeline for neurodevelopmental EEG data.

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“…For example, one study used spectral and nonlinear features of EEG signals to predict the diagnosis of ASD in infancy [11]. However, collecting high-quality EEG data in infants or young participants is subject to substantial methodological hurdles contingent upon the experimental settings and the intended analyses [12]. A whole-brain functional magnetic resonance imaging (fMRI) study [13] of children with ASD and children with developmental language disorders revealed decreased functional connectivity between the frontal and parietal-occipital regions and between the anterior and posterior insular cortex and the limbic cortex.…”

Section: Introductionmentioning

confidence: 99%

ECG Recordings as Predictors of Very Early Autism Likelihood: A Machine Learning Approach

et al. 2023

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In recent years, there has been a rise in the prevalence of autism spectrum disorder (ASD). The diagnosis of ASD requires behavioral observation and standardized testing completed by highly trained experts. Early intervention for ASD can begin as early as 1–2 years of age, but ASD diagnoses are not typically made until ages 2–5 years, thus delaying the start of intervention. There is an urgent need for non-invasive biomarkers to detect ASD in infancy. While previous research using physiological recordings has focused on brain-based biomarkers of ASD, this study investigated the potential of electrocardiogram (ECG) recordings as an ASD biomarker in 3–6-month-old infants. We recorded the heart activity of infants at typical and elevated familial likelihood for ASD during naturalistic interactions with objects and caregivers. After obtaining the ECG signals, features such as heart rate variability (HRV) and sympathetic and parasympathetic activities were extracted. Then we evaluated the effectiveness of multiple machine learning classifiers for classifying ASD likelihood. Our findings support our hypothesis that infant ECG signals contain important information about ASD familial likelihood. Amongthe various machine learning algorithms tested, KNN performed best according to sensitivity (0.70 ± 0.117), F1-score (0.689 ± 0.124), precision (0.717 ± 0.128), accuracy (0.70 ± 0.117, p-value = 0.02), and ROC (0.686 ± 0.122, p-value = 0.06). These results suggest that ECG signals contain relevant information about the likelihood of an infant developing ASD. Future studies should consider the potential of information contained in ECG, and other indices of autonomic control, for the development of biomarkers of ASD in infancy.

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Challenges and new perspectives of developmental cognitive EEG studies

Cited by 17 publications

References 260 publications

Real‐time vibration monitoring and analysis of agricultural tractor drivers using an IoT‐based system

Real‐time vibration monitoring and analysis of agricultural tractor drivers using an IoT‐based system

RELAX-Jr: An Automated Pre-Processing Pipeline for Developmental EEG Recordings

ECG Recordings as Predictors of Very Early Autism Likelihood: A Machine Learning Approach

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