Automated detection of conduct disorder and attention deficit hyperactivity disorder using decomposition and nonlinear techniques with EEG signals

Tor, Hui Tian; Ooi, Chui Ping; Lim-Ashworth, Nikki; Wei, Joel Koh En; Vicnesh, Jahmunah; Oh, Shu Lih; Acharya, U. Rajendra; Fung, Daniel

doi:10.1016/j.cmpb.2021.105941

Cited by 85 publications

(42 citation statements)

References 60 publications

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“…Deep learning is a subfield of machine learning in which large data is used to train these models, which can also predict outcomes with high accuracies. Both models are commonly used in the diagnosis of some neurological disorders, such as autism [41,42], ADHD [43,44] and depression [45][46][47], with high accuracies. The models are either fed with images obtained from computerised tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET) scans or electroencephalogram (EEG) signals for the diagnosis of neurological disorders.…”

Section: Conventional Methods Using Aimentioning

confidence: 99%

Artificial Intelligence Enabled Personalised Assistive Tools to Enhance Education of Children with Neurodevelopmental Disorders—A Review

Barua

Vicnesh

Gururajan

et al. 2022

IJERPH

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Mental disorders (MDs) with onset in childhood or adolescence include neurodevelopmental disorders (NDDs) (intellectual disability and specific learning disabilities, such as dyslexia, attention deficit disorder (ADHD), and autism spectrum disorders (ASD)), as well as a broad range of mental health disorders (MHDs), including anxiety, depressive, stress-related and psychotic disorders. There is a high co-morbidity of NDDs and MHDs. Globally, there have been dramatic increases in the diagnosis of childhood-onset mental disorders, with a 2- to 3-fold rise in prevalence for several MHDs in the US over the past 20 years. Depending on the type of MD, children often grapple with social and communication deficits and difficulties adapting to changes in their environment, which can impact their ability to learn effectively. To improve outcomes for children, it is important to provide timely and effective interventions. This review summarises the range and effectiveness of AI-assisted tools, developed using machine learning models, which have been applied to address learning challenges in students with a range of NDDs. Our review summarises the evidence that AI tools can be successfully used to improve social interaction and supportive education. Based on the limitations of existing AI tools, we provide recommendations for the development of future AI tools with a focus on providing personalised learning for individuals with NDDs.

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Section: Conventional Methods Using Aimentioning

confidence: 99%

Artificial Intelligence Enabled Personalised Assistive Tools to Enhance Education of Children with Neurodevelopmental Disorders—A Review

Barua

Vicnesh

Gururajan

et al. 2022

IJERPH

Self Cite

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“…EEG-based computational methods are widely used to diagnose and identify various diseases [ 12 ]. Epilepsy diagnosis [ 13 – 15 ], seizures and strokes [ 16 , 17 ], Alzheimer [ 18 , 19 ], convulsions, depression [ 20 ], attention deficit disorder [ 21 ], biometric fields [ 22 ], and fatigue diagnosis [ 23 , 24 ] are among the applications of EEG.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Approach for MS Diagnosis Through Nonlinear EEG Descriptors and Metaheuristic Optimized Classification Learning

Mohseni

Moghaddasi

2022

Computational Intelligence and Neuroscience

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Multiple sclerosis (MS), a disease of the central nervous system, affects the white matter of the brain. Neurologists interpret magnetic resonance images that are often complicated, time-consuming, and contradictory. Using EEG signals, this disease can be analyzed and diagnosed more accurately. However, it is imperative that MS be diagnosed by specialists using assistive technology. Until now, a few methods have been proposed in this field that are sometimes associated with different challenges in analysis. This paper presents a hybrid approach to MS diagnosis in order to decrease classification error rates. Using the proposed method, EEG descriptors in both the time and frequency domains are analyzed. After the feature extraction stage, a modified version of the ant colony optimization method (m-ACO) was used to select the appropriate subset of features. Then, the support vector machine is used to determine whether the disease exists. A metaheuristic algorithm adjusts the support vector machine’s parameters to withstand overfitting challenges. Despite a limited number of input channels, significant classification accuracy has been achieved using wavelet analysis techniques, dividing all five subbands of EEG signals, signal windowing, and extracting efficient features from the data. Additionally, alpha, beta, and gamma bands of the signal are important, and the accuracy, sensitivity, and specificity levels are higher than 98.5%. Compared to similar MS diagnostic methods, the proposed method achieved significantly higher diagnostic accuracy. Application and implementation of this method can be effective in treating neurological diseases, including multiple sclerosis.

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“…Applications of reading task classification include measuring attention and engagement (Miller, 2015; Abdelrahman et al, 2019), detecting proper reading versus skimming (Biedert et al, 2012), as well as applications related to intent recognition within brain computer interfaces (Schalk et al, 2008). Other studies have demonstrated that recognizing reading patterns for estimating reading effort can improve the diagnosis of reading impairments such as dyslexia (Rello and Ballesteros, 2015; Raatikainen et al, 2021) and attention deficit disorder (Tor et al, 2021). Furthermore, it has been shown that using EEG and eye-tracking signals facilitates the prediction workload (Lobo et al, 2016) and investigation of language learning (Notaro and Diamond, 2018).…”

Section: Introductionmentioning

confidence: 99%

The ZuCo Benchmark on Cross-Subject Reading Task Classification with EEG and Eye-Tracking Data

Hollenstein¹,

Tröndle

Płomecka

et al. 2022

Preprint

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We present a new machine learning benchmark for reading task classification with the goal of advancing EEG and eye-tracking research at the intersection between computational language processing and cognitive neuroscience. The benchmark task consists of a cross-subject classification to distinguish between two reading paradigms: normal reading and task-specific reading. The data for the benchmark is based on the Zurich Cognitive Language Processing Corpus (ZuCo 2.0), which provides simultaneous eye-tracking and EEG signals from natural reading. The training dataset is publicly available, and we present a newly recorded hidden testset. We provide multiple solid baseline methods for this task and discuss future improvements. We release our code and provide an easy-to-use interface to evaluate new approaches with an accompanying public leaderboard: www.zuco-benchmark.com.HighlightsWe present a new machine learning benchmark for reading task classification with the goal of advancing EEG and eye-tracking research.We provide an interface to evaluate new approaches with an accompanying public leaderboard.The benchmark task consists of a cross-subject classification to distinguish between two reading paradigms: normal reading and task-specific reading.The data is based on the Zurich Cognitive Language Processing Corpus of simultaneous eye-tracking and EEG signals from natural reading.

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Automated detection of conduct disorder and attention deficit hyperactivity disorder using decomposition and nonlinear techniques with EEG signals

Cited by 85 publications

References 60 publications

Artificial Intelligence Enabled Personalised Assistive Tools to Enhance Education of Children with Neurodevelopmental Disorders—A Review

Artificial Intelligence Enabled Personalised Assistive Tools to Enhance Education of Children with Neurodevelopmental Disorders—A Review

A Hybrid Approach for MS Diagnosis Through Nonlinear EEG Descriptors and Metaheuristic Optimized Classification Learning

The ZuCo Benchmark on Cross-Subject Reading Task Classification with EEG and Eye-Tracking Data

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