  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. In light of this, we propose a
novel multi-task generative adversarial network to convert the individual's EEG
signals evoked by geometrical shapes to the original geometry. First, we adopt
a Convolutional Neural Network (CNN) to learn highly informative latent
representation for the raw EEG signals, which is vital for the subsequent shape
reconstruction. Next, we build the discriminator based on multi-task learning
to distinguish and classify fake samples simultaneously, where the mutual
promotion between different tasks improves the quality of the recovered shapes.
Then, we propose a semantic alignment constraint in order to force the
synthesized samples to approach the real ones in pixel-level, thus producing
more compelling shapes. The proposed approach is evaluated over a local dataset
and the results show that our model outperforms the competitive
state-of-the-art baselines.
