Multi-task Generative Adversarial Learning on Geometrical Shape Reconstruction from EEG Brain Signals

Understand

Synthesizing geometrical shapes from human brain activities is an interesting and meaningful but very challenging topic.

• Recently, the advancements of deep generative models like Generative Adversarial Networks (GANs) have supported the object generation from neurological signals.
• However, the Electroencephalograph (EEG)-based shape generation still suffer from the low realism problem.
• In particular, the generated geometrical shapes lack clear edges and fail to contain necessary details.