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Leveraging pretrained vision transformers for classifying alcohol use disorder using raw resting-state EEG

Alcohol Use Disorder (AUD) is a prevalent neuropsychiatric condition affecting about 28 million adults in the USA, with few objective biomarkers 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). The initial cohort included a total of 5402 recordings from 2710 participants (aged 13-83, mean age 24; 1512 males and 1198 females). Minimal preprocessing was applied to preserve the raw EEG features. We utilized EEGViT, a hybrid deep learning architecture that combines convolutional patch embedding with a Vision Transformer (ViT) pretrained on ImageNet, thereby enabling end-to-end learning directly …

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