Exploring Data Augmentation for Stroke Severity Prediction: How Generative AI Enhances EEG Signal Classification

In clinical datasets, data augmentation is often avoided because it can remove important unusual values, known as outliers, which are important for understanding diseases accurately. However, clinical EEG datasets often suffer from two key limitations: small sample sizes and class imbalance, which significantly reduce classification performance. To overcome these challenges, this study applies generative AI-based data augmentation techniques with a carefully designed hybrid pipeline that effectively enhances performance even on limited EEG data. Using EEG signals from 50 brain stroke patients,

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