  Electroencephalography (EEG) signals are known to manifest differential
patterns when individuals visually concentrate on different objects. In this
work, we present an end-to-end digital fabrication system, Brain2Object, to
print the 3D object that an individual is observing by decoding visually-evoked
brain signals. We propose a unified training framework that combines
multi-class Common Spatial Pattern and Convolutional Neural Networks to support
the backend computation. We learn the dynamical graph representations of brain
signals to accurately capture the structural information among EEG channels. A
user-friendly interface is developed as the system front end. Brain2Object
presents a streamlined end-to-end workflow that can serve as a template for
deeper integration of BCI technologies to assist with our routine activities.
  The proposed system is evaluated extensively using offline experiments and
through an online demonstrator. The experimental results show that our approach
can achieve the recognition accuracy of 92.58% on a benchmark dataset and
75.23% on a locally collected dataset. Moreover, our method consistently
outperforms a wide range of baseline and state-of-the-art approaches. The
proof-of-concept corroborates the practicality of our approach and illustrates
the ease with which such a system could be deployed.
