Volta

Leveraging Pretrained Vision Transformers for classifying Alcohol Use Disorder using Raw Resting-State EEG

Alcohol Use Disorder (AUD) is a prevalent and debilitating neuropsychiatric condition characterized by compulsive alcohol consumption, impaired control, and negative emotional states, affecting about 28 million adults in the United States. Despite its significant public health burden, there are few objective biomarkers and no reliable neurophysiological tools to assist in its clinical diagnosis. In this study, we investigated the potential of deep learning to classify individuals with AUD using raw resting-state electroencephalogram (EEG) data. EEG recordings were obtained from the Collaborative Study on the Genetics of Alcoholism (COGA), a large, longitudinal, multi-site dataset. The initial cohort included a total of 5,402 recordings from 2,710 participants (aged 12-83, mean age 24; 1,338 males and 1,372 females). To reduce confounding factors, we …

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