Analysis of EEG brain connectivity of children with ADHD using graph theory and directional information transfer

Ekhlasi, Ali; Nasrabadi, Ali Motie; Mohammadi, Mohammad-Reza

doi:10.1515/bmt-2022-0100

Cited by 9 publications

(4 citation statements)

References 56 publications

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“…The limitations of the current systems prevent their performance in classifying AHDA using the EGG dataset. This is evident from the findings of previous studies [23,24], which reported an accuracy of 93.91% and 91% using SVM and graphic neural networks based on the accuracy measures. Therefore, we have built an upgraded system aimed at boosting the accuracy of the existing method.…”

Section: Introductionsupporting

confidence: 69%

Developing System-Based Artificial Intelligence Models for Detecting the Attention Deficit Hyperactivity Disorder

Alkahtani,

Aldhyani,

Ahmed

et al. 2023

Mathematics

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This study presents a novel methodology for automating the classification of pediatric ADHD using electroencephalogram (EEG) biomarkers through machine learning and deep learning techniques. The primary objective is to develop accurate EEG-based screening tools to aid clinical diagnosis and enable early intervention for ADHD. The proposed system utilizes a publicly available dataset consisting of raw EEG recordings from 61 individuals with ADHD and 60 control subjects during a visual attention task. The methodology involves meticulous preprocessing of raw EEG recordings to isolate brain signals and extract informative features, including time, frequency, and entropy signal characteristics. The feature selection techniques, including least absolute shrinkage and selection operator (LASSO) regularization and recursive elimination, were applied to identify relevant variables and enhance generalization. The obtained features are processed by employing various machine learning and deep learning algorithms, namely CatBoost, Random Forest Decision Trees, Convolutional Neural Networks (CNNs), and Long Short-Term Memory Networks (LSTMs). The empirical results of the proposed algorithms highlight the effectiveness of feature selection approaches in matching informative biomarkers with optimal model classes. The convolutional neural network model achieves superior testing accuracy of 97.75% using LASSO-regularized biomarkers, underscoring the strengths of deep learning and customized feature optimization. The proposed framework advances EEG analysis to uncover discriminative patterns, significantly contributing to the field of ADHD screening and diagnosis. The suggested methodology achieved high performance compared with different existing systems based on AI approaches for diagnosing ADHD.

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Section: Introductionsupporting

confidence: 69%

Developing System-Based Artificial Intelligence Models for Detecting the Attention Deficit Hyperactivity Disorder

Alkahtani,

Aldhyani,

Ahmed

et al. 2023

Mathematics

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“…Finally, we are taking into consideration connectionism here, because when other theories of mind lack neuroscientific integration [26] and a consequent philosophical coherence, connectionism has been distinctive. After all, it made a strong effort to implement AI and neuroscience into a philosophical theory.…”

Section: Connectionismmentioning

confidence: 99%

Problems of Connectionism

Vassallo,

Sattin,

Parati

et al. 2024

Philosophies

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The relationship between philosophy and science has always been complementary. Today, while science moves increasingly fast and philosophy shows some problems in catching up with it, it is not always possible to ignore such relationships, especially in some disciplines such as philosophy of mind, cognitive science, and neuroscience. However, the methodological procedures used to analyze these data are based on principles and assumptions that require a profound dialogue between philosophy and science. Following these ideas, this work aims to raise the problems that a classical connectionist theory can cause and problematize them in a cognitive framework, considering both philosophy and cognitive sciences but also the disciplines that are near to them, such as AI, computer sciences, and linguistics. For this reason, we embarked on an analysis of both the computational and theoretical problems that connectionism currently has. The second aim of this work is to advocate for collaboration between neuroscience and philosophy of mind because the promotion of deeper multidisciplinarity seems necessary in order to solve connectionism’s problems. In fact, we believe that the problems that we detected can be solved by a thorough investigation at both a theoretical and an empirical level, and they do not represent an impasse but rather a starting point from which connectionism should learn and be updated while keeping its original and profoundly convincing core.

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“…PLI is useful for studying transient phase synchrony, especially in event-related analysis [63]. T. S. Bearden et al [27] in resting state EEG using the functional magnetic resonance image (fMRI) data of children with ADHD [28][29][30], and FC of default mode network (DMN) and dorsal attention network (DAN) in children with ADHD [31], another example was that [32] combined resting state functional magnetic resonance imaging (rsfMRI) and FC to study the default mode network (DMN). In [32], the event-related potential (ERP) was investigated from childhood to adulthood during cognitive tasks; in another paper, a deep learning model was applied to classify ADHD [24] by using a publicly available fMRI database of adolescents with ADHD.…”

Section: Introductionmentioning

confidence: 99%

Temporal Alpha Dissimilarity of ADHD Brain Network in Comparison With CPT and CATA

Lin,

Fan,

Tzou

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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Attention deficit hyperactivity disorder (ADHD) is a chronic neurological and psychiatric disorder that affects children during their development. To find neural patterns for ADHD, and provide subjective features as decision references to assist specialist and physicians. Many studies devoted to investigate the neural dynamics of brain of resting-state or continuous performance tests (CPT) with EEG or functional magnetic resonance image (fMRI). The present study use coherence, which is one of the functional connectivity (FC) method, to analyze the neural patterns of children and adolescents (8-16 years old) under CPT and continuous auditory test of attention (CATA) task.In the meantime, electroencephalography (EEG) oscillations were recorded by a wireless brain-computer interface (BCI). 72 children were enrolled, of which 53 participants were diagnosed as ADHD and 19 presented to be typical developing (TD). The experimental results exhibited higher difference in alpha and theta bands between the TD group and the ADHD group. While the differences between the TD group and the ADHD group in all four frequency domains were greater than under CPT conditions. Statistically significant differences (p<0.05) were observed between the ADHD and TD groups in the alpha rhythm during the CATA task in the short-range of coherence. For the temporal lobe FC during the CATA task, the TD group exhibited statistically significantly FC (p<0.05) in the alpha rhythm compared to the ADHD group. These findings offering new possibilities for more techniques and diagnostic methods in finding more ADHD features. The differences in alpha and beta frequencies were more pronounced in the ADHD group during the CPT task compared to the CATA task. Additionally, the disparities in brain activity were more evident across delta, theta, alpha and beta frequency domains when the task given was a CATA as opposed to a CPT. The findings presented the underlying mechanisms of the FC differences between children and adolescents with ADHD. Moreover, these findings should extend to use machine learning approaches to assist the ADHD classification and diagnosis.

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Analysis of EEG brain connectivity of children with ADHD using graph theory and directional information transfer

Cited by 9 publications

References 56 publications

Developing System-Based Artificial Intelligence Models for Detecting the Attention Deficit Hyperactivity Disorder

Developing System-Based Artificial Intelligence Models for Detecting the Attention Deficit Hyperactivity Disorder

Problems of Connectionism

Temporal Alpha Dissimilarity of ADHD Brain Network in Comparison With CPT and CATA

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