Benchmarking Deep Learning Optimizers for the classification of Event-Related Potentials based Brain-Computer Interface EEG data

The lack of theoretical convergence guarantees for gradient-based deep learning optimizers across diverse classification and generation tasks leaves the choice of the optimizer to arbitrary considerations. Developing new optimizers requires heavy empirical benchmarking; moreover, their performance improvements are observed on large datasets and their generalizations to small datasets are underexpl...