An EEG-based functional connectivity measure for automatic detection of alcohol use disorder

作者：

Highlights：

• A machine learning (ML) model has been proposed involving the synchronization likelihood (SL) features.

• The proposed model automatically classifies Alcohol Use Disorder patients and healthy controls.

• The paper concluded that the SL features can be used as biomarkers for screening the AUD patients.

摘要

•A machine learning (ML) model has been proposed involving the synchronization likelihood (SL) features.•The proposed model automatically classifies Alcohol Use Disorder patients and healthy controls.•The paper concluded that the SL features can be used as biomarkers for screening the AUD patients.

论文关键词：Alcohol use disorder (AUD),Alcohol abuse (AA),Alcohol dependence (AD),Electroencephalography (EEG),Resting-state EEG (REEG),Synchronization likelihood

论文评审过程：Received 30 May 2017, Revised 15 August 2017, Accepted 10 November 2017, Available online 21 November 2017, Version of Record 5 February 2018.

论文官网地址：https://doi.org/10.1016/j.artmed.2017.11.002