Classification of Global Brain States by Resampling and Data Augmentation of EEG

The brain exhibits multiple global states that can be distinguished by wave patterns in electroencephalogram (EEG). Global brain states during the wake have been of much interest among neurologists recently, but some of the states occur in low percentages, such as less than 10% of all epochs. This scarcity makes automatic classification difficult due to class imbalance. By introducing resampling and data augmentation techniques, we developed a system based on ResNet that automatically classifies highly imbalanced brain state data. Namely, we tested oversampling, undersampling, and SMOTE on single-channel EEG recordings obtained from mice. The results showed the effectiveness of dealing with class imbalance, opening possibilities for further analysis of global brain states during the wake.