ArticleCognitive neurodynamics2017
An EEG-based machine learning method to screen alcohol use disorder.
Article in Cognitive neurodynamics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 2 of them syntheses that pooled it.
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Who cites it
30 citing papers in PubMed, 2 syntheses or guidelines pooled it, 76 citations in OpenAlex.
- Advances in AI-Driven EEG Analysis for Neurological and Oculomotor Disorders: A Systematic Review.Biosensors · 2025Pooled it
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- Cortical Complexity Alterations in Methamphetamine, Cannabis, and Opioid Users: An EEG-Based Analysis.Journal of biomedical physics & engineering · 2026Article
- Leveraging Pretrained Vision Transformers for classifying Alcohol Use Disorder using Raw Resting-State EEG.bioRxiv : the preprint server for biology · 2026Article
- Leveraging Machine Learning to Advance Alcohol Research: Current Applications, Challenges, and Opportunities.Alcohol research : current reviews · 2026Review
- Repeated alcohol drinking in mice is associated with bidirectional alterations in corticostriatal coherence.Neuropharmacology · 2025Article
- Clinical, genomic, and neurophysiological correlates of lifetime suicide attempts among individuals with an alcohol use disorder.medRxiv : the preprint server for health sciences · 2024Article
- Clinical, genomic, and neurophysiological correlates of lifetime suicide attempts among individuals with alcohol dependence.Research square · 2024Article
- COVID-19 pandemic stressors are associated with reported increases in frequency of drunkenness among individuals with a history of alcohol use disorder.Translational psychiatry · 2023Article
- Machine learning-based classifying of risk-takers and risk-aversive individuals using resting-state EEG data: A pilot feasibility study.Brain and behavior · 2023Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Improved HHT-microstate analysis of EEG in nicotine addicts.Frontiers in neuroscience · 2023Article
- Alcohol dependence inpatients classification with GLM and hierarchical clustering integration using fMRI data of alcohol multiple scenario cues.Experimental brain research · 2022Article
- Predicting risk for Alcohol Use Disorder using longitudinal data with multimodal biomarkers and family history: a machine learning study.Molecular psychiatry · 2021Article
- Chronic pain as a brain imbalance between pain input and pain suppression.Brain communications · 2021Article
- Article
- Real-Time Stress Assessment Using Sliding Window Based Convolutional Neural Network.Sensors (Basel, Switzerland) · 2020Article
- A brain-computer interface for the continuous, real-time monitoring of working memory load in real-world environments.Cognitive neurodynamics · 2020Article
- Random Forest Classification of Alcohol Use Disorder Using EEG Source Functional Connectivity, Neuropsychological Functioning, and Impulsivity Measures.Behavioral sciences (Basel, Switzerland) · 2020Article
- Random Forest Classification of Alcohol Use Disorder Using fMRI Functional Connectivity, Neuropsychological Functioning, and Impulsivity Measures.Brain sciences · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Screening alcohol use disorder (AUD) patients has been challenging due to the subjectivity involved in the process. Hence, robust and objective methods are needed to automate the screening of AUD patients. In this paper, a machine learning method is proposed that utilized resting-state electroencephalography (EEG)-derived features as input data to classify the AUD patients and healthy controls and to perform automatic screening of AUD patients. In this context, the EEG data were recorded during 5 min of eyes closed and 5 min of eyes open conditions. For this purpose, 30 AUD patients and 15 aged-matched healthy controls were recruited. After preprocessing the EEG data, EEG features such as inter-hemispheric coherences and spectral power for EEG delta, theta, alpha, beta and gamma bands were computed involving 19 scalp locations. The selection of most discriminant features was performed with a rank-based feature selection method assigning a weight value to each feature according to a criterion, i.e., receiver operating characteristics curve. For example, a feature with large weight was considered more relevant to the target labels than a feature with less weight. Therefore, a reduced set of most discriminant features was identified and further be utilized during classification of AUD patients and healthy controls. As results, the inter-hemispheric coherences between the brain regions were found significantly different between the study groups and provided high classification efficiency (
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.