MOTOR IMAGERY CLASSIFICATION FOR BCI USING STOCKWELL TRANSFORM, DEEP METRIC LEARNING, AND DCNN WITH MIXUP AUGMENTATION - ALWASITI, HAIDER (2021)
Inter-individual EEG variability is a major issue limiting the performance of Brain-Computer Interface (BCI) classifiers. However, most previous deep learning (DL) models are still using the dataset of multiple subjects to train a single model due to the limited augmentation techniques available for EEG signals, and the difficulty of collecting large EEG datasets from each subject. Building a DL model that can be trained on an extremely small EEG training set of a single subject presents an interesting challenge that this work is trying to address. The deep metric learning (DML) model is known for the ability to converge on a small dataset, however, this kind of model has not been studied yet on BCI EEG signals.