Speaker
Description
Artificial intelligence and neuroscience offer complementary perspectives on learning, adaptation, and variability. This talk presents recently published work on deep-learning models based on variational autoencoders, designed to capture the complex dynamics of EEG signals and their substantial inter- and intra-subject variability. By learning informative latent representations, these models can support high-fidelity EEG reconstruction, anomaly detection, and the characterization of alterations associated with artefacts and pathological conditions. The talk will also introduce selected ongoing activities of the newly launched WavesLab, spanning brain–computer interfaces, multimodal analysis of physiological signals, and the use of generative AI for literature research within an Open Science framework.