On the use of pairwise distance learning for brain signal classification with limited observations

David Calhas, Enrique Romero, Rui Henriques

Abstract: Highlights•Dedicated Siamese neural networks improve learnability from limited EEG recordings.•Cosine-loss measuring distance of spectral content is more robust to noisy EEG data.•Pairing schema and spectral processing handle spatiotemporal nature of EEG data.•Comprehensive models of discriminative brain patterning found from resting state EEG.