The Neuroscientific Basis of Flow: Learning Progress Guides Task Engagement and Cognitive Control DOI Creative Commons
Hairong Lu, Dimitri van der Linden, Arnold B. Bakker

et al.

NeuroImage, Journal Year: 2025, Volume and Issue: unknown, P. 121076 - 121076

Published: Feb. 1, 2025

People often strive for deep engagement in activities, a state typically associated with feelings of flow - full task absorption accompanied by sense control and enjoyment. The intrinsic factors driving such facilitating subjective remain unclear. Building on computational theories motivation, this study examines how learning progress predicts directs cognitive control. Results showed that engagement, indicated low distractibility, is function progress. Electroencephalography data further revealed enhanced proactive preparation (e.g., reduced pre-stimulus contingent negativity variance parietal alpha desynchronization) improved feedback processing increased P3b amplitude desynchronization). impact observed at the task-block goal-episode levels, but not trial level. This suggests shapes over extended periods as accumulates. These findings highlight critical role sustaining goal-directed behavior.

Language: Английский

Theta activity and cognitive functioning: Integrating evidence from resting-state and task-related developmental electroencephalography (EEG) research DOI Creative Commons
Enda Tan, Sonya V. Troller‐Renfree, Santiago Morales

et al.

Developmental Cognitive Neuroscience, Journal Year: 2024, Volume and Issue: 67, P. 101404 - 101404

Published: June 1, 2024

The theta band is one of the most prominent frequency bands in electroencephalography (EEG) power spectrum and presents an interesting paradox: while elevated during resting state linked to lower cognitive abilities children adolescents, increased tasks associated with higher performance. Why does power, measured versus tasks, show differential correlations functioning? This review provides integrated account functional correlates across different contexts. We first present evidence that correlated executive functioning, attentional abilities, language skills, IQ. Next, we research showing increases memory, attention, control, these processes better Finally, discuss potential explanations for between resting/task-related offer suggestions future this area.

Language: Английский

Citations

23
Graph Neural Network-Based EEG Classification: A Survey DOI Creative Commons
Dominik Klepl, Min Wu, Fei He

et al.

IEEE Transactions on Neural Systems and Rehabilitation Engineering, Journal Year: 2024, Volume and Issue: 32, P. 493 - 503

Published: Jan. 1, 2024

Graph neural networks (GNN) are increasingly used to classify EEG for tasks such as emotion recognition, motor imagery and neurological diseases disorders. A wide range of methods have been proposed design GNN-based classifiers. Therefore, there is a need systematic review categorisation these approaches. We exhaustively search the published literature on this topic derive several categories comparison. These highlight similarities differences among methods. The results suggest prevalence spectral graph convolutional layers over spatial. Additionally, we identify standard forms node features, with most popular being raw signal differential entropy. Our summarise emerging trends in approaches classification. Finally, discuss promising research directions, exploring potential transfer learning appropriate modelling cross-frequency interactions.

Language: Английский

Citations

20
Brain network modulation in response to directional and Non-Directional Cues: Insights from EEG connectivity and graph theory DOI
Fabrizio Vecchio, Francesca Miraglia, Chiara Pappalettera

et al.

Clinical Neurophysiology, Journal Year: 2025, Volume and Issue: 171, P. 146 - 153

Published: Jan. 25, 2025

Language: Английский

Citations

2
Annual Research Review: Developmental pathways linking early behavioral inhibition to later anxiety DOI Creative Commons
Nathan A. Fox, Selin Zeytinoglu, Emilio A. Valadez

et al.

Journal of Child Psychology and Psychiatry, Journal Year: 2022, Volume and Issue: 64(4), P. 537 - 561

Published: Sept. 19, 2022

Behavioral Inhibition is a temperament identified in the first years of life that enhances risk for development anxiety during late childhood and adolescence. Amongst children characterized with this temperament, only around 40 percent go on to develop disorders, meaning more than half these do not. Over past 20 years, research has documented within‐child socio‐contextual factors support differing developmental pathways. This review provides historical perspective documenting origins its biological correlates, enhance or mitigate anxiety. We as well, findings from two longitudinal cohorts have moderators behavioral inhibition understanding pathways Research led us Detection Dual Control (DDC) framework understand trajectories among behaviorally inhibited children. In review, we use explain why how specific cognitive influence differential versus resilience.

Language: Английский

Citations

39
Infant neuroscience: how to measure brain activity in the youngest minds DOI
Nicholas B. Turk‐Browne, Richard Ν. Aslin

Trends in Neurosciences, Journal Year: 2024, Volume and Issue: 47(5), P. 338 - 354

Published: April 3, 2024

Language: Английский

Citations

9
Functional and effective EEG connectivity patterns in Alzheimer’s disease and mild cognitive impairment: a systematic review DOI Creative Commons
Elizabeth R. Paitel,

Christian Otteman,

Mary C. Polking

et al.

Frontiers in Aging Neuroscience, Journal Year: 2025, Volume and Issue: 17

Published: Feb. 12, 2025

Background Alzheimer’s disease (AD) might be best conceptualized as a disconnection syndrome, such that symptoms may largely attributable to disrupted communication between brain regions, rather than deterioration within discrete systems. EEG is uniquely capable of directly and non-invasively measuring neural activity with precise temporal resolution; connectivity quantifies the relationships signals in different regions. research on AD mild cognitive impairment (MCI), often considered prodromal phase AD, has produced mixed results yet synthesized for comprehensive review. Thus, we performed systematic review MCI participants compared cognitively healthy older adult controls. Methods We searched PsycINFO, PubMed, Web Science peer-reviewed studies English EEG, connectivity, MCI/AD relative Of 1,344 initial matches, 124 articles were ultimately included Results The primarily analyzed coherence, phase-locked, graph theory metrics. influence factors demographics, design, approach was integrated discussed. An overarching pattern emerged lower both controls, which most prominent alpha band, consistent AD. In minority reporting greater theta band commonly implicated MCI, followed by alpha. overall prevalence effects indicate its potential provide insight into nuanced changes associated AD-related networks, caveat during resting state where dominant frequency. When reported it task engagement, suggesting compensatory resources employed. common rest, engagement already exhausted. Conclusion highlighted powerful tool advance understanding communication. address need including demographic methodological details, using source space extending this work adults risk toward advancing early detection intervention.

Language: Английский

Citations

1
Machine Learning Algorithms for Epilepsy Detection Based on Published EEG Databases: A Systematic Review DOI Creative Commons
Ανδρέας Μιλτιάδους, Katerina D. Tzimourta, Νικόλαος Γιαννακέας

et al.

IEEE Access, Journal Year: 2022, Volume and Issue: 11, P. 564 - 594

Published: Dec. 26, 2022

Epilepsy is the only neurological condition for which electroencephalography (EEG) primary diagnostic and important prognostic clinical tool. However, manual inspection of EEG signals a time-consuming procedure neurologists. Thus, intense research has been made on creating machine learning methodologies automated epilepsy detection. Also, many or medical facilities have published databases epileptic to accommodate this effort. The vast number studies concerning detection with makes systematic review necessary. It presents detailed evaluation signal processing classification employed different provides valuable insights future work. 190 were included in according PRISMA guidelines, acquired from literature search PubMed, Scopus, ScienceDirect IEEE Xplore 1st May 2021. Studies examined based Signal Transformation technique, methodology database evaluation. Along other findings, increasing tendency employ Convolutional Neural Networks that use combination Time-Frequency decomposition images noticed.

Language: Английский

Citations

27
Maximizing the potential of EEG as a developmental neuroscience tool DOI Creative Commons
George A. Buzzell, Santiago Morales, Emilio A. Valadez

et al.

Developmental Cognitive Neuroscience, Journal Year: 2023, Volume and Issue: 60, P. 101201 - 101201

Published: Jan. 27, 2023

Language: Английский

Citations

16
Exploring new horizons in neuroscience disease detection through innovative visual signal analysis DOI Creative Commons
Nisreen Said Amer, Samir Brahim Belhaouari

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Feb. 20, 2024

Abstract Brain disorders pose a substantial global health challenge, persisting as leading cause of mortality worldwide. Electroencephalogram (EEG) analysis is crucial for diagnosing brain disorders, but it can be challenging medical practitioners to interpret complex EEG signals and make accurate diagnoses. To address this, our study focuses on visualizing in format easily understandable by professionals deep learning algorithms. We propose novel time–frequency (TF) transform called the Forward–Backward Fourier (FBFT) utilize convolutional neural networks (CNNs) extract meaningful features from TF images classify disorders. introduce concept eye-naked classification, which integrates domain-specific knowledge clinical expertise into classification process. Our demonstrates effectiveness FBFT method, achieving impressive accuracies across multiple using CNN-based classification. Specifically, we achieve 99.82% epilepsy, 95.91% Alzheimer’s disease (AD), 85.1% murmur, 100% mental stress Furthermore, context naked-eye 78.6%, 71.9%, 82.7%, 91.0% AD, stress, respectively. Additionally, incorporate mean correlation coefficient (mCC) based channel selection method enhance accuracy further. By combining these innovative approaches, enhances visualization signals, providing with deeper understanding images. This research has potential bridge gap between image visual interpretation, better detection improved patient care field neuroscience.

Language: Английский

Citations

6
Towards complex multi-component pulse signal with strong noise: Deconvolution and time–frequency assisted mode decomposition DOI
Gang Shi, Chengjin Qin, Zhinan Zhang

et al.

Mechanical Systems and Signal Processing, Journal Year: 2024, Volume and Issue: 212, P. 111274 - 111274

Published: March 4, 2024

Language: Английский

Citations

6